Genetic Algorithm for Initial Orbit Determination with Too Short Arc

NASA Astrophysics Data System (ADS)

Li, Xin-ran; Wang, Xin

2017-01-01

A huge quantity of too-short-arc (TSA) observational data have been obtained in sky surveys of space objects. However, reasonable results for the TSAs can hardly be obtained with the classical methods of initial orbit determination (IOD). In this paper, the IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selections of the optimized variables and the corresponding genetic operators for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc

NASA Astrophysics Data System (ADS)

Li, X. R.; Wang, X.

2016-01-01

The sky surveys of space objects have obtained a huge quantity of too-short-arc (TSA) observation data. However, the classical method of initial orbit determination (IOD) can hardly get reasonable results for the TSAs. The IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selection of optimizing variables as well as the corresponding genetic operator for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.

Evolutionary Optimization of Yagi-Uda Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Colombano, Silvano P.

2001-01-01

Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

Dental implant customization using numerical optimization design and 3-dimensional printing fabrication of zirconia ceramic.

PubMed

Cheng, Yung-Chang; Lin, Deng-Huei; Jiang, Cho-Pei; Lin, Yuan-Min

2017-05-01

This study proposes a new methodology for dental implant customization consisting of numerical geometric optimization and 3-dimensional printing fabrication of zirconia ceramic. In the numerical modeling, exogenous factors for implant shape include the thread pitch, thread depth, maximal diameter of implant neck, and body size. Endogenous factors are bone density, cortical bone thickness, and non-osseointegration. An integration procedure, including uniform design method, Kriging interpolation and genetic algorithm, is applied to optimize the geometry of dental implants. The threshold of minimal micromotion for optimization evaluation was 100 μm. The optimized model is imported to the 3-dimensional slurry printer to fabricate the zirconia green body (powder is bonded by polymer weakly) of the implant. The sintered implant is obtained using a 2-stage sintering process. Twelve models are constructed according to uniform design method and simulated the micromotion behavior using finite element modeling. The result of uniform design models yields a set of exogenous factors that can provide the minimal micromotion (30.61 μm), as a suitable model. Kriging interpolation and genetic algorithm modified the exogenous factor of the suitable model, resulting in 27.11 μm as an optimization model. Experimental results show that the 3-dimensional slurry printer successfully fabricated the green body of the optimization model, but the accuracy of sintered part still needs to be improved. In addition, the scanning electron microscopy morphology is a stabilized t-phase microstructure, and the average compressive strength of the sintered part is 632.1 MPa. Copyright © 2016 John Wiley & Sons, Ltd.

Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Sen, S. K.

2007-01-01

Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *

Fuzzy multi objective transportation problem – evolutionary algorithm approach

NASA Astrophysics Data System (ADS)

Karthy, T.; Ganesan, K.

2018-04-01

This paper deals with fuzzy multi objective transportation problem. An fuzzy optimal compromise solution is obtained by using Fuzzy Genetic Algorithm. A numerical example is provided to illustrate the methodology.

Methodology of Numerical Optimization for Orbital Parameters of Binary Systems

NASA Astrophysics Data System (ADS)

Araya, I.; Curé, M.

2010-02-01

The use of a numerical method of maximization (or minimization) in optimization processes allows us to obtain a great amount of solutions. Therefore, we can find a global maximum or minimum of the problem, but this is only possible if we used a suitable methodology. To obtain the global optimum values, we use the genetic algorithm called PIKAIA (P. Charbonneau) and other four algorithms implemented in Mathematica. We demonstrate that derived orbital parameters of binary systems published in some papers, based on radial velocity measurements, are local minimum instead of global ones.

Using a genetic algorithm to optimize a water-monitoring network for accuracy and cost effectiveness

NASA Astrophysics Data System (ADS)

Julich, R. J.

2004-05-01

The purpose of this project is to determine the optimal spatial distribution of water-monitoring wells to maximize important data collection and to minimize the cost of managing the network. We have employed a genetic algorithm (GA) towards this goal. The GA uses a simple fitness measure with two parts: the first part awards a maximal score to those combinations of hydraulic head observations whose net uncertainty is closest to the value representing all observations present, thereby maximizing accuracy; the second part applies a penalty function to minimize the number of observations, thereby minimizing the overall cost of the monitoring network. We used the linear statistical inference equation to calculate standard deviations on predictions from a numerical model generated for the 501-observation Death Valley Regional Flow System as the basis for our uncertainty calculations. We have organized the results to address the following three questions: 1) what is the optimal design strategy for a genetic algorithm to optimize this problem domain; 2) what is the consistency of solutions over several optimization runs; and 3) how do these results compare to what is known about the conceptual hydrogeology? Our results indicate the genetic algorithms are a more efficient and robust method for solving this class of optimization problems than have been traditional optimization approaches.

Multivariable optimization of an auto-thermal ammonia synthesis reactor using genetic algorithm

NASA Astrophysics Data System (ADS)

Anh-Nga, Nguyen T.; Tuan-Anh, Nguyen; Tien-Dung, Vu; Kim-Trung, Nguyen

2017-09-01

The ammonia synthesis system is an important chemical process used in the manufacture of fertilizers, chemicals, explosives, fibers, plastics, refrigeration. In the literature, many works approaching the modeling, simulation and optimization of an auto-thermal ammonia synthesis reactor can be found. However, they just focus on the optimization of the reactor length while keeping the others parameters constant. In this study, the other parameters are also considered in the optimization problem such as the temperature of feed gas enters the catalyst zone. The optimal problem requires the maximization of a multivariable objective function which subjects to a number of equality constraints involving the solution of coupled differential equations and also inequality constraints. The solution of an optimization problem can be found through, among others, deterministic or stochastic approaches. The stochastic methods, such as evolutionary algorithm (EA), which is based on natural phenomenon, can overcome the drawbacks such as the requirement of the derivatives of the objective function and/or constraints, or being not efficient in non-differentiable or discontinuous problems. Genetic algorithm (GA) which is a class of EA, exceptionally simple, robust at numerical optimization and is more likely to find a true global optimum. In this study, the genetic algorithm is employed to find the optimum profit of the process. The inequality constraints were treated using penalty method. The coupled differential equations system was solved using Runge-Kutta 4th order method. The results showed that the presented numerical method could be applied to model the ammonia synthesis reactor. The optimum economic profit obtained from this study are also compared to the results from the literature. It suggests that the process should be operated at higher temperature of feed gas in catalyst zone and the reactor length is slightly longer.

Optimal design of dampers within seismic structures

NASA Astrophysics Data System (ADS)

Ren, Wenjie; Qian, Hui; Song, Wali; Wang, Liqiang

2009-07-01

An improved multi-objective genetic algorithm for structural passive control system optimization is proposed. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. For a constrained problem, the dominance-based penalty function method is advanced, containing information on an individual's status (feasible or infeasible), position in a search space, and distance from a Pareto optimal set. The proposed approach is used for the optimal designs of a six-storey building with shape memory alloy dampers subjected to earthquake. The number and position of dampers are chosen as the design variables. The number of dampers and peak relative inter-storey drift are considered as the objective functions. Numerical results generate a set of non-dominated solutions.

A comparative study of electrochemical machining process parameters by using GA and Taguchi method

NASA Astrophysics Data System (ADS)

Soni, S. K.; Thomas, B.

2017-11-01

In electrochemical machining quality of machined surface strongly depend on the selection of optimal parameter settings. This work deals with the application of Taguchi method and genetic algorithm using MATLAB to maximize the metal removal rate and minimize the surface roughness and overcut. In this paper a comparative study is presented for drilling of LM6 AL/B4C composites by comparing the significant impact of numerous machining process parameters such as, electrolyte concentration (g/l),machining voltage (v),frequency (hz) on the response parameters (surface roughness, material removal rate and over cut). Taguchi L27 orthogonal array was chosen in Minitab 17 software, for the investigation of experimental results and also multiobjective optimization done by genetic algorithm is employed by using MATLAB. After obtaining optimized results from Taguchi method and genetic algorithm, a comparative results are presented.

Automated Calibration For Numerical Models Of Riverflow

NASA Astrophysics Data System (ADS)

Fernandez, Betsaida; Kopmann, Rebekka; Oladyshkin, Sergey

2017-04-01

Calibration of numerical models is fundamental since the beginning of all types of hydro system modeling, to approximate the parameters that can mimic the overall system behavior. Thus, an assessment of different deterministic and stochastic optimization methods is undertaken to compare their robustness, computational feasibility, and global search capacity. Also, the uncertainty of the most suitable methods is analyzed. These optimization methods minimize the objective function that comprises synthetic measurements and simulated data. Synthetic measurement data replace the observed data set to guarantee an existing parameter solution. The input data for the objective function derivate from a hydro-morphological dynamics numerical model which represents an 180-degree bend channel. The hydro- morphological numerical model shows a high level of ill-posedness in the mathematical problem. The minimization of the objective function by different candidate methods for optimization indicates a failure in some of the gradient-based methods as Newton Conjugated and BFGS. Others reveal partial convergence, such as Nelder-Mead, Polak und Ribieri, L-BFGS-B, Truncated Newton Conjugated, and Trust-Region Newton Conjugated Gradient. Further ones indicate parameter solutions that range outside the physical limits, such as Levenberg-Marquardt and LeastSquareRoot. Moreover, there is a significant computational demand for genetic optimization methods, such as Differential Evolution and Basin-Hopping, as well as for Brute Force methods. The Deterministic Sequential Least Square Programming and the scholastic Bayes Inference theory methods present the optimal optimization results. keywords: Automated calibration of hydro-morphological dynamic numerical model, Bayesian inference theory, deterministic optimization methods.

Optimization of the Hartmann-Shack microlens array

NASA Astrophysics Data System (ADS)

de Oliveira, Otávio Gomes; de Lima Monteiro, Davies William

2011-04-01

In this work we propose to optimize the microlens-array geometry for a Hartmann-Shack wavefront sensor. The optimization makes possible that regular microlens arrays with a larger number of microlenses are replaced by arrays with fewer microlenses located at optimal sampling positions, with no increase in the reconstruction error. The goal is to propose a straightforward and widely accessible numerical method to calculate an optimized microlens array for a known aberration statistics. The optimization comprises the minimization of the wavefront reconstruction error and/or the number of necessary microlenses in the array. We numerically generate, sample and reconstruct the wavefront, and use a genetic algorithm to discover the optimal array geometry. Within an ophthalmological context, as a case study, we demonstrate that an array with only 10 suitably located microlenses can be used to produce reconstruction errors as small as those of a 36-microlens regular array. The same optimization procedure can be employed for any application where the wavefront statistics is known.

Genetically Engineered Microelectronic Infrared Filters

NASA Technical Reports Server (NTRS)

Cwik, Tom; Klimeck, Gerhard

1998-01-01

A genetic algorithm is used for design of infrared filters and in the understanding of the material structure of a resonant tunneling diode. These two components are examples of microdevices and nanodevices that can be numerically simulated using fundamental mathematical and physical models. Because the number of parameters that can be used in the design of one of these devices is large, and because experimental exploration of the design space is unfeasible, reliable software models integrated with global optimization methods are examined The genetic algorithm and engineering design codes have been implemented on massively parallel computers to exploit their high performance. Design results are presented for the infrared filter showing new and optimized device design. Results for nanodevices are presented in a companion paper at this workshop.

Joint optimization of maintenance, buffers and machines in manufacturing lines

NASA Astrophysics Data System (ADS)

Nahas, Nabil; Nourelfath, Mustapha

2018-01-01

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.

Processor design optimization methodology for synthetic vision systems

NASA Astrophysics Data System (ADS)

Wren, Bill; Tarleton, Norman G.; Symosek, Peter F.

1997-06-01

Architecture optimization requires numerous inputs from hardware to software specifications. The task of varying these input parameters to obtain an optimal system architecture with regard to cost, specified performance and method of upgrade considerably increases the development cost due to the infinitude of events, most of which cannot even be defined by any simple enumeration or set of inequalities. We shall address the use of a PC-based tool using genetic algorithms to optimize the architecture for an avionics synthetic vision system, specifically passive millimeter wave system implementation.

Research on numerical method for multiple pollution source discharge and optimal reduction program

NASA Astrophysics Data System (ADS)

Li, Mingchang; Dai, Mingxin; Zhou, Bin; Zou, Bin

2018-03-01

In this paper, the optimal method for reduction program is proposed by the nonlinear optimal algorithms named that genetic algorithm. The four main rivers in Jiangsu province, China are selected for reducing the environmental pollution in nearshore district. Dissolved inorganic nitrogen (DIN) is studied as the only pollutant. The environmental status and standard in the nearshore district is used to reduce the discharge of multiple river pollutant. The research results of reduction program are the basis of marine environmental management.

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

Supercontinuum optimization for dual-soliton based light sources using genetic algorithms in a grid platform.

PubMed

Arteaga-Sierra, F R; Milián, C; Torres-Gómez, I; Torres-Cisneros, M; Moltó, G; Ferrando, A

2014-09-22

We present a numerical strategy to design fiber based dual pulse light sources exhibiting two predefined spectral peaks in the anomalous group velocity dispersion regime. The frequency conversion is based on the soliton fission and soliton self-frequency shift occurring during supercontinuum generation. The optimization process is carried out by a genetic algorithm that provides the optimum input pulse parameters: wavelength, temporal width and peak power. This algorithm is implemented in a Grid platform in order to take advantage of distributed computing. These results are useful for optical coherence tomography applications where bell-shaped pulses located in the second near-infrared window are needed.

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

NASA Astrophysics Data System (ADS)

Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

2018-03-01

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

Genetic algorithms for the vehicle routing problem

NASA Astrophysics Data System (ADS)

Volna, Eva

2016-06-01

The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

Exchange inlet optimization by genetic algorithm for improved RBCC performance

NASA Astrophysics Data System (ADS)

Chorkawy, G.; Etele, J.

2017-09-01

A genetic algorithm based on real parameter representation using a variable selection pressure and variable probability of mutation is used to optimize an annular air breathing rocket inlet called the Exchange Inlet. A rapid and accurate design method which provides estimates for air breathing, mixing, and isentropic flow performance is used as the engine of the optimization routine. Comparison to detailed numerical simulations show that the design method yields desired exit Mach numbers to within approximately 1% over 75% of the annular exit area and predicts entrained air massflows to between 1% and 9% of numerically simulated values depending on the flight condition. Optimum designs are shown to be obtained within approximately 8000 fitness function evaluations in a search space on the order of 106. The method is also shown to be able to identify beneficial values for particular alleles when they exist while showing the ability to handle cases where physical and aphysical designs co-exist at particular values of a subset of alleles within a gene. For an air breathing engine based on a hydrogen fuelled rocket an exchange inlet is designed which yields a predicted air entrainment ratio within 95% of the theoretical maximum.

A genetic algorithm for optimization of neural network capable of learning to search for food in a maze

NASA Astrophysics Data System (ADS)

Budilova, E. V.; Terekhin, A. T.; Chepurnov, S. A.

1994-09-01

A hypothetical neural scheme is proposed that ensures efficient decision making by an animal searching for food in a maze. Only the general structure of the network is fixed; its quantitative characteristics are found by numerical optimization that simulates the process of natural selection. Selection is aimed at maximization of the expected number of descendants, which is directly related to the energy stored during the reproductive cycle. The main parameters to be optimized are the increments of the interneuronal links and the working-memory constants.

A numerical and theoretical study on the aerodynamics of a rhinoceros beetle (Trypoxlyus dichotomus) and optimization of its wing kinematics in hover

NASA Astrophysics Data System (ADS)

Oh, Sehyeong; Lee, Boogeon; Park, Hyungmin; Choi, Haecheon

2017-11-01

We investigate a hovering rhinoceros beetle using numerical simulation and blade element theory. Numerical simulations are performed using an immersed boundary method. In the simulation, the hindwings are modeled as a rigid flat plate, and three-dimensionally scanned elytra and body are used. The results of simulation indicate that the lift force generated by the hindwings alone is sufficient to support the weight, and the elytra generate negligible lift force. Considering the hindwings only, we present a blade element model based on quasi-steady assumptions to identify the mechanisms of aerodynamic force generation and power expenditure in the hovering flight of a rhinoceros beetle. We show that the results from the present blade element model are in excellent agreement with numerical ones. Based on the current blade element model, we find the optimal wing kinematics minimizing the aerodynamic power requirement using a hybrid optimization algorithm combining a clustering genetic algorithm with a gradient-based optimizer. We show that the optimal wing kinematics reduce the aerodynamic power consumption, generating enough lift force to support the weight. This research was supported by a Grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD130070ID) and NRF-2016R1E1A1A02921549 of the MSIP of Korea.

Design of the BRISC study: a multicentre controlled clinical trial to optimize the communication of breast cancer risks in genetic counselling.

PubMed

Ockhuysen-Vermey, Caroline F; Henneman, Lidewij; van Asperen, Christi J; Oosterwijk, Jan C; Menko, Fred H; Timmermans, Daniëlle R M

2008-10-03

Understanding risks is considered to be crucial for informed decision-making. Inaccurate risk perception is a common finding in women with a family history of breast cancer attending genetic counseling. As yet, it is unclear how risks should best be communicated in clinical practice. This study protocol describes the design and methods of the BRISC (Breast cancer RISk Communication) study evaluating the effect of different formats of risk communication on the counsellee's risk perception, psychological well-being and decision-making regarding preventive options for breast cancer. The BRISC study is designed as a pre-post-test controlled group intervention trial with repeated measurements using questionnaires. The intervention-an additional risk consultation-consists of one of 5 conditions that differ in the way counsellee's breast cancer risk is communicated: 1) lifetime risk in numerical format (natural frequencies, i.e. X out of 100), 2) lifetime risk in both numerical format and graphical format (population figures), 3) lifetime risk and age-related risk in numerical format, 4) lifetime risk and age-related risk in both numerical format and graphical format, and 5) lifetime risk in percentages. Condition 6 is the control condition in which no intervention is given (usual care). Participants are unaffected women with a family history of breast cancer attending one of three participating clinical genetic centres in the Netherlands. The BRISC study allows for an evaluation of the effects of different formats of communicating breast cancer risks to counsellees. The results can be used to optimize risk communication in order to improve informed decision-making among women with a family history of breast cancer. They may also be useful for risk communication in other health-related services. Current Controlled Trials ISRCTN14566836.

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

NASA Astrophysics Data System (ADS)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

Inference of Vohradský's Models of Genetic Networks by Solving Two-Dimensional Function Optimization Problems

PubMed Central

Kimura, Shuhei; Sato, Masanao; Okada-Hatakeyama, Mariko

2013-01-01

The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations. PMID:24386175

Optimization design of multiphase pump impeller based on combined genetic algorithm and boundary vortex flux diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Jin-ya; Cai, Shu-jie; Li, Yong-jiang; Li, Yong-jiang; Zhang, Yong-xue

2017-12-01

A novel optimization design method for the multiphase pump impeller is proposed through combining the quasi-3D hydraulic design (Q3DHD), the boundary vortex flux (BVF) diagnosis, and the genetic algorithm (GA). The BVF diagnosis based on the Q3DHD is used to evaluate the objection function. Numerical simulations and hydraulic performance tests are carried out to compare the impeller designed only by the Q3DHD method and that optimized by the presented method. The comparisons of both the flow fields simulated under the same condition show that (1) the pressure distribution in the optimized impeller is more reasonable and the gas-liquid separation is more efficiently inhibited, (2) the scales of the gas pocket and the vortex decrease remarkably for the optimized impeller, (3) the unevenness of the BVF distributions near the shroud of the original impeller is effectively eliminated in the optimized impeller. The experimental results show that the differential pressure and the maximum efficiency of the optimized impeller are increased by 4% and 2.5%, respectively. Overall, the study indicates that the optimization design method proposed in this paper is feasible.

Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy

NASA Astrophysics Data System (ADS)

Piccininni, A.; Palumbo, G.; Franco, A. Lo; Sorgente, D.; Tricarico, L.; Russello, G.

2018-05-01

The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature. A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. A preliminary investigation was necessary to correctly tune the laser parameters (focus length, spot dimension) to effectively obtain the annealed state. Optimal process parameters were then obtained coupling a 2D FE model with an optimization platform managed by a multi-objective genetic algorithm. The optimal solution (i.e. able to maximize the LDR) in terms of blankholder force and extent of the annealed region was thus evaluated and validated through experimental trials. A good matching between experimental and numerical results was found. The optimal solution allowed to obtain an LDR of the locally heat treated blank larger than the one of the material either in the wrought condition (H32) either in the annealed condition (H111).

Optimization model of vaccination strategy for dengue transmission

NASA Astrophysics Data System (ADS)

Widayani, H.; Kallista, M.; Nuraini, N.; Sari, M. Y.

2014-02-01

Dengue fever is emerging tropical and subtropical disease caused by dengue virus infection. The vaccination should be done as a prevention of epidemic in population. The host-vector model are modified with consider a vaccination factor to prevent the occurrence of epidemic dengue in a population. An optimal vaccination strategy using non-linear objective function was proposed. The genetic algorithm programming techniques are combined with fourth-order Runge-Kutta method to construct the optimal vaccination. In this paper, the appropriate vaccination strategy by using the optimal minimum cost function which can reduce the number of epidemic was analyzed. The numerical simulation for some specific cases of vaccination strategy is shown.

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

2002-01-01

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

A novel method for the production of core-shell microparticles by inverse gelation optimized with artificial intelligent tools.

PubMed

Rodríguez-Dorado, Rosalia; Landín, Mariana; Altai, Ayça; Russo, Paola; Aquino, Rita P; Del Gaudio, Pasquale

2018-03-01

Numerous studies have been focused on hydrophobic compounds encapsulation as oils. In fact, oils can provide numerous health benefits as synergic ingredient combined with other hydrophobic active ingredients. However, stable microparticles for pharmaceutical purposes are difficult to achieve when commonly techniques are used. In this work, sunflower oil was encapsulated in calcium-alginate capsules by prilling technique in co-axial configuration. Core-shell beads were produced by inverse gelation directly at the nozzle using a w/o emulsion containing aqueous calcium chloride solution in sunflower oil pumped through the inner nozzle while an aqueous alginate solution, coming out from the annular nozzle, produced the beads shell. To optimize process parameters artificial intelligence tools were proposed to optimize the numerous prilling process variables. Homogeneous and spherical microcapsules with narrow size distribution and a thin alginate shell were obtained when the parameters as w/o constituents, polymer concentrations, flow rates and frequency of vibration were optimized by two commercial software, FormRules® and INForm®, which implement neurofuzzy logic and Artificial Neural Networks together with genetic algorithms, respectively. This technique constitutes an innovative approach for hydrophobic compounds microencapsulation. Copyright © 2018 Elsevier B.V. All rights reserved.

Co-state initialization for the minimum-time low-thrust trajectory optimization

NASA Astrophysics Data System (ADS)

Taheri, Ehsan; Li, Nan I.; Kolmanovsky, Ilya

2017-05-01

This paper presents an approach for co-state initialization which is a critical step in solving minimum-time low-thrust trajectory optimization problems using indirect optimal control numerical methods. Indirect methods used in determining the optimal space trajectories typically result in two-point boundary-value problems and are solved by single- or multiple-shooting numerical methods. Accurate initialization of the co-state variables facilitates the numerical convergence of iterative boundary value problem solvers. In this paper, we propose a method which exploits the trajectory generated by the so-called pseudo-equinoctial and three-dimensional finite Fourier series shape-based methods to estimate the initial values of the co-states. The performance of the approach for two interplanetary rendezvous missions from Earth to Mars and from Earth to asteroid Dionysus is compared against three other approaches which, respectively, exploit random initialization of co-states, adjoint-control transformation and a standard genetic algorithm. The results indicate that by using our proposed approach the percent of the converged cases is higher for trajectories with higher number of revolutions while the computation time is lower. These features are advantageous for broad trajectory search in the preliminary phase of mission designs.

Exponential H ∞ Synchronization of Chaotic Cryptosystems Using an Improved Genetic Algorithm

PubMed Central

Hsiao, Feng-Hsiag

2015-01-01

This paper presents a systematic design methodology for neural-network- (NN-) based secure communications in multiple time-delay chaotic (MTDC) systems with optimal H ∞ performance and cryptography. On the basis of the Improved Genetic Algorithm (IGA), which is demonstrated to have better performance than that of a traditional GA, a model-based fuzzy controller is then synthesized to stabilize the MTDC systems. A fuzzy controller is synthesized to not only realize the exponential synchronization, but also achieve optimal H ∞ performance by minimizing the disturbance attenuation level. Furthermore, the error of the recovered message is stated by using the n-shift cipher and key. Finally, a numerical example with simulations is given to demonstrate the effectiveness of our approach. PMID:26366432

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

NASA Astrophysics Data System (ADS)

Dao, Son Duy; Abhary, Kazem; Marian, Romeo

2017-06-01

Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to "learn" from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.

Genetic algorithm based active vibration control for a moving flexible smart beam driven by a pneumatic rod cylinder

NASA Astrophysics Data System (ADS)

Qiu, Zhi-cheng; Shi, Ming-li; Wang, Bin; Xie, Zhuo-wei

2012-05-01

A rod cylinder based pneumatic driving scheme is proposed to suppress the vibration of a flexible smart beam. Pulse code modulation (PCM) method is employed to control the motion of the cylinder's piston rod for simultaneous positioning and vibration suppression. Firstly, the system dynamics model is derived using Hamilton principle. Its standard state-space representation is obtained for characteristic analysis, controller design, and simulation. Secondly, a genetic algorithm (GA) is applied to optimize and tune the control gain parameters adaptively based on the specific performance index. Numerical simulations are performed on the pneumatic driving elastic beam system, using the established model and controller with tuned gains by GA optimization process. Finally, an experimental setup for the flexible beam driven by a pneumatic rod cylinder is constructed. Experiments for suppressing vibrations of the flexible beam are conducted. Theoretical analysis, numerical simulation and experimental results demonstrate that the proposed pneumatic drive scheme and the adopted control algorithms are feasible. The large amplitude vibration of the first bending mode can be suppressed effectively.

Design of isolated buildings with S-FBI system subjected to near-fault earthquakes using NSGA-II algorithm

NASA Astrophysics Data System (ADS)

Ozbulut, O. E.; Silwal, B.

2014-04-01

This study investigates the optimum design parameters of a superelastic friction base isolator (S-FBI) system through a multi-objective genetic algorithm and performance-based evaluation approach. The S-FBI system consists of a flat steel- PTFE sliding bearing and a superelastic NiTi shape memory alloy (SMA) device. Sliding bearing limits the transfer of shear across the isolation interface and provides damping from sliding friction. SMA device provides restoring force capability to the isolation system together with additional damping characteristics. A three-story building is modeled with S-FBI isolation system. Multiple-objective numerical optimization that simultaneously minimizes isolation-level displacements and superstructure response is carried out with a genetic algorithm (GA) in order to optimize S-FBI system. Nonlinear time history analyses of the building with S-FBI system are performed. A set of 20 near-field ground motion records are used in numerical simulations. Results show that S-FBI system successfully control response of the buildings against near-fault earthquakes without sacrificing in isolation efficacy and producing large isolation-level deformations.

Optimal locations and orientations of piezoelectric transducers on cylindrical shell based on gramians of contributed and undesired Rayleigh-Ritz modes using genetic algorithm

NASA Astrophysics Data System (ADS)

Biglar, Mojtaba; Mirdamadi, Hamid Reza; Danesh, Mohammad

2014-02-01

In this study, the active vibration control and configurational optimization of a cylindrical shell are analyzed by using piezoelectric transducers. The piezoelectric patches are attached to the surface of the cylindrical shell. The Rayleigh-Ritz method is used for deriving dynamic modeling of cylindrical shell and piezoelectric sensors and actuators based on the Donnel-Mushtari shell theory. The major goal of this study is to find the optimal locations and orientations of piezoelectric sensors and actuators on the cylindrical shell. The optimization procedure is designed based on desired controllability and observability of each contributed and undesired mode. Further, in order to limit spillover effects, the residual modes are taken into consideration. The optimization variables are the positions and orientations of piezoelectric patches. Genetic algorithm is utilized to evaluate the optimal configurations. In this article, for improving the maximum power and capacity of actuators for amplitude depreciation of negative velocity feedback strategy, we have proposed a new control strategy, called "Saturated Negative Velocity Feedback Rule (SNVF)". The numerical results show that the optimization procedure is effective for vibration reduction, and specifically, by locating actuators and sensors in their optimal locations and orientations, the vibrations of cylindrical shell are suppressed more quickly.

Comparing genetic algorithm and particle swarm optimization for solving capacitated vehicle routing problem

NASA Astrophysics Data System (ADS)

Iswari, T.; Asih, A. M. S.

2018-04-01

In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.

Cityscape genetics: structural vs. functional connectivity of an urban lizard population.

PubMed

Beninde, Joscha; Feldmeier, Stephan; Werner, Maike; Peroverde, Daniel; Schulte, Ulrich; Hochkirch, Axel; Veith, Michael

2016-10-01

Functional connectivity is essential for the long-term persistence of populations. However, many studies assess connectivity with a focus on structural connectivity only. Cityscapes, namely urban landscapes, are particularly dynamic and include numerous potential anthropogenic barriers to animal movements, such as roads, traffic or buildings. To assess and compare structural connectivity of habitats and functional connectivity of gene flow of an urban lizard, we here combined species distribution models (SDMs) with an individual-based landscape genetic optimization procedure. The most important environmental factors of the SDMs are structural diversity and substrate type, with high and medium levels of structural diversity as well as open and rocky/gravel substrates contributing most to structural connectivity. By contrast, water cover was the best model of all environmental factors following landscape genetic optimization. The river is thus a major barrier to gene flow, while of the typical anthropogenic factors only buildings showed an effect. Nonetheless, using SDMs as a basis for landscape genetic optimization provided the highest ranked model for functional connectivity. Optimizing SDMs in this way can provide a sound basis for models of gene flow of the cityscape, and elsewhere, while presence-only and presence-absence modelling approaches showed differences in performance. Additionally, interpretation of results based on SDM factor importance can be misleading, dictating more thorough analyses following optimization of SDMs. Such approaches can be adopted for management strategies, for example aiming to connect native common wall lizard populations or disconnect them from non-native introduced populations, which are currently spreading in many cities in Central Europe. © 2016 John Wiley & Sons Ltd.

A Platform for Antenna Optimization with Numerical Electromagnetics Code Incorporated with Genetic Algorithms

DTIC Science & Technology

2006-03-01

have been the concentration of many literature compositions [12, 30, 38, 39, 49, 53]. Van Veldhuizen et. al. [53] improved the geometries of wire...Electric Waves”. J. IEE (Japan), volume 47, 273–282. March 1926. 53. Veldhuizen , David A. Van , Brian S. Sandlin, Rober E. Marmelstein, Gary B. Lam- ont, and

Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

2016-05-01

One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.

A Comparison of Trajectory Optimization Methods for the Impulsive Minimum Fuel Rendezvous Problem

NASA Technical Reports Server (NTRS)

Hughes, Steven P.; Mailhe, Laurie M.; Guzman, Jose J.

2002-01-01

In this paper we present a comparison of optimization approaches to the minimum fuel rendezvous problem. Both indirect and direct methods are compared for a variety of test cases. The indirect approach is based on primer vector theory. The direct approaches are implemented numerically and include Sequential Quadratic Programming (SQP), Quasi-Newton, Simplex, Genetic Algorithms, and Simulated Annealing. Each method is applied to a variety of test cases including, circular to circular coplanar orbits, LEO to GEO, and orbit phasing in highly elliptic orbits. We also compare different constrained optimization routines on complex orbit rendezvous problems with complicated, highly nonlinear constraints.

On the asymptotic optimality and improved strategies of SPTB heuristic for open-shop scheduling problem

NASA Astrophysics Data System (ADS)

Bai, Danyu; Zhang, Zhihai

2014-08-01

This article investigates the open-shop scheduling problem with the optimal criterion of minimising the sum of quadratic completion times. For this NP-hard problem, the asymptotic optimality of the shortest processing time block (SPTB) heuristic is proven in the sense of limit. Moreover, three different improvements, namely, the job-insert scheme, tabu search and genetic algorithm, are introduced to enhance the quality of the original solution generated by the SPTB heuristic. At the end of the article, a series of numerical experiments demonstrate the convergence of the heuristic, the performance of the improvements and the effectiveness of the quadratic objective.

Hereditary arrhythmias and cardiomyopathies: decision-making about genetic testing.

PubMed

Louis, Clauden; Calamaro, Emily; Vinocur, Jeffrey M

2018-01-01

The modern field of clinical genetics has advanced beyond the traditional teachings familiar to most practicing cardiologists. Increased understanding of the roles of genetic testing may improve uptake and appropriateness of use. Clinical genetics has become integral to the management of patients with hereditary arrhythmia and cardiomyopathy diagnoses. Depending on the condition, genetic testing may be useful for diagnosis, prognosis, treatment, family screening, and reproductive planning. However, genetic testing is a powerful tool with potential for underuse, overuse, and misuse. In the absence of a substantial body of literature on how these guidelines are applied in clinical practice, we use a case-based approach to highlight key lessons and pitfalls. Importantly, in many scenarios genetic testing has become the standard of care supported by numerous class I recommendations; genetic counselors can improve accessibility to and appropriate use and application of testing. Optimal management of hereditary arrhythmias and cardiomyopathies incorporates genetic testing, applied as per consensus guidelines, with involvement of a multidisciplinary team.

Optimization design of energy deposition on single expansion ramp nozzle

NASA Astrophysics Data System (ADS)

Ju, Shengjun; Yan, Chao; Wang, Xiaoyong; Qin, Yupei; Ye, Zhifei

2017-11-01

Optimization design has been widely used in the aerodynamic design process of scramjets. The single expansion ramp nozzle is an important component for scramjets to produces most of thrust force. A new concept of increasing the aerodynamics of the scramjet nozzle with energy deposition is presented. The essence of the method is to create a heated region in the inner flow field of the scramjet nozzle. In the current study, the two-dimensional coupled implicit compressible Reynolds Averaged Navier-Stokes and Menter's shear stress transport turbulence model have been applied to numerically simulate the flow fields of the single expansion ramp nozzle with and without energy deposition. The numerical results show that the proposal of energy deposition can be an effective method to increase force characteristics of the scramjet nozzle, the thrust coefficient CT increase by 6.94% and lift coefficient CN decrease by 26.89%. Further, the non-dominated sorting genetic algorithm coupled with the Radial Basis Function neural network surrogate model has been employed to determine optimum location and density of the energy deposition. The thrust coefficient CT and lift coefficient CN are selected as objective functions, and the sampling points are obtained numerically by using a Latin hypercube design method. The optimized thrust coefficient CT further increase by 1.94%, meanwhile, the optimized lift coefficient CN further decrease by 15.02% respectively. At the same time, the optimized performances are in good and reasonable agreement with the numerical predictions. The findings suggest that scramjet nozzle design and performance can benefit from the application of energy deposition.

Optimal procedures for home visits — A case study

NASA Astrophysics Data System (ADS)

Alves, Filipe; Fernandes, Florbela P.; Pereira, Ana I.; Fernandes, Adília

2017-07-01

In Portugal the population is ageing. Therefore, the provision of health care at patients' home is becoming an important social and health area; this health service is provided by professional teams (usually composed by nurses) of the Health Centers. Nowadays, the scheduling of the visits is made manually. The proposal of this work is to do the scheduling automatically in order to minimize the overall time spent by the professional teams in the visiting activity. In this work the genetic algorithm was used to solve the optimization problem. Some numerical results are presented.

A hybrid artificial bee colony algorithm for numerical function optimization

NASA Astrophysics Data System (ADS)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu\\xA0search

NASA Astrophysics Data System (ADS)

Yeh, Cheng-Ta; Lin, Yi-Kuei; Yang, Jo-Yun

2018-07-01

Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.

Multi-objective optimization in spatial planning: Improving the effectiveness of multi-objective evolutionary algorithms (non-dominated sorting genetic algorithm II)

NASA Astrophysics Data System (ADS)

Karakostas, Spiros

2015-05-01

The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.

A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics

NASA Astrophysics Data System (ADS)

Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.

2015-12-01

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.

Numerical and Experimental Validation of the Optimization Methodologies for a Wing-Tip Structure Equipped with Conventional and Morphing Ailerons =

NASA Astrophysics Data System (ADS)

Koreanschi, Andreea

In order to answer the problem of 'how to reduce the aerospace industry's environment footprint?' new morphing technologies were developed. These technologies were aimed at reducing the aircraft's fuel consumption through reduction of the wing drag. The morphing concept used in the present research consists of replacing the conventional aluminium upper surface of the wing with a flexible composite skin for morphing abilities. For the ATR-42 'Morphing wing' project, the wing models were manufactured entirely from composite materials and the morphing region was optimized for flexibility. In this project two rigid wing models and an active morphing wing model were designed, manufactured and wind tunnel tested. For the CRIAQ MDO 505 project, a full scale wing-tip equipped with two types of ailerons, conventional and morphing, was designed, optimized, manufactured, bench and wind tunnel tested. The morphing concept was applied on a real wing internal structure and incorporated aerodynamic, structural and control constraints specific to a multidisciplinary approach. Numerical optimization, aerodynamic analysis and experimental validation were performed for both the CRIAQ MDO 505 full scale wing-tip demonstrator and the ATR-42 reduced scale wing models. In order to improve the aerodynamic performances of the ATR-42 and CRIAQ MDO 505 wing airfoils, three global optimization algorithms were developed, tested and compared. The three algorithms were: the genetic algorithm, the artificial bee colony and the gradient descent. The algorithms were coupled with the two-dimensional aerodynamic solver XFoil. XFoil is known for its rapid convergence, robustness and use of the semi-empirical e n method for determining the position of the flow transition from laminar to turbulent. Based on the performance comparison between the algorithms, the genetic algorithm was chosen for the optimization of the ATR-42 and CRIAQ MDO 505 wing airfoils. The optimization algorithm was improved during the CRIAQ MDO 505 project for convergence speed by introducing a two-step cross-over function. Structural constraints were introduced in the algorithm at each aero-structural optimization interaction, allowing a better manipulation of the algorithm and giving it more capabilities of morphing combinations. The CRIAQ MDO 505 project envisioned a morphing aileron concept for the morphing upper surface wing. For this morphing aileron concept, two optimization methods were developed. The methods used the already developed genetic algorithm and each method had a different design concept. The first method was based on the morphing upper surface concept, using actuation points to achieve the desired shape. The second method was based on the hinge rotation concept of the conventional aileron but applied at multiple nodes along the aileron camber to achieve the desired shape. Both methods were constrained by manufacturing and aerodynamic requirements. The purpose of the morphing aileron methods was to obtain an aileron shape with a smoother pressure distribution gradient during deflection than the conventional aileron. The aerodynamic optimization results were used for the structural optimization and design of the wing, particularly the flexible composite skin. Due to the structural changes performed on the initial wing-tip structure, an aeroelastic behaviour analysis, more specific on flutter phenomenon, was performed. The analyses were done to ensure the structural integrity of the wing-tip demonstrator during wind tunnel tests. Three wind tunnel tests were performed for the CRIAQ MDO 505 wing-tip demonstrator at the IAR-NRC subsonic wind tunnel facility in Ottawa. The first two tests were performed for the wing-tip equipped with conventional aileron. The purpose of these tests was to validate the control system designed for the morphing upper surface, the numerical optimization and aerodynamic analysis and to evaluate the optimization efficiency on the boundary layer behaviour and the wing drag. The third set of wind tunnel tests was performed on the wing-tip equipped with a morphing aileron. The purpose of this test was to evaluate the performances of the morphing aileron, in conjunction with the active morphing upper surface, and their effect on the lift, drag and boundary layer behaviour. Transition data, obtained from Infrared Thermography, and pressure data, extracted from Kulite and pressure taps recordings, were used to validate the numerical optimization and aerodynamic performances of the wing-tip demonstrator. A set of wind tunnel tests was performed on the ATR-42 rigid wing models at the Price-Paidoussis subsonic wind tunnel at Ecole de technologie Superieure. The results from the pressure taps recordings were used to validate the numerical optimization. A second derivative of the pressure distribution method was applied to evaluate the transition region on the upper surface of the wing models for comparison with the numerical transition values. (Abstract shortened by ProQuest.).

Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin; Cheng, Runwei

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

Optimization of microphysics in the Unified Model, using the Micro-genetic algorithm.

NASA Astrophysics Data System (ADS)

Jang, J.; Lee, Y.; Lee, H.; Lee, J.; Joo, S.

2016-12-01

This study focuses on parameter optimization of microphysics in the Unified Model (UM) using the Micro-genetic algorithm (Micro-GA). We need the optimization of microphysics in UM. Because, Microphysics in the Numerical Weather Prediction (NWP) model is important to Quantitative Precipitation Forecasting (QPF). The Micro-GA searches for optimal parameters on the basis of fitness function. The five parameters are chosen. The target parameters include x1, x2 related to raindrop size distribution, Cloud-rain correlation coefficient, Surface droplet number and Droplet taper height. The fitness function is based on the skill score that is BIAS and Critical Successive Index (CSI). An interface between UM and Micro-GA is developed and applied to three precipitation cases in Korea. The cases are (ⅰ) heavy rainfall in the Southern area because of typhoon NAKRI, (ⅱ) heavy rainfall in the Youngdong area, and (ⅲ) heavy rainfall in the Seoul metropolitan area. When the optimized result is compared to the control result (using the UM default value, CNTL), the optimized result leads to improvements in precipitation forecast, especially for heavy rainfall of the late forecast time. Also, we analyze the skill score of precipitation forecasts in terms of various thresholds of CNTL, Optimized result, and experiments on each optimized parameter for five parameters. Generally, the improvement is maximized when the five optimized parameters are used simultaneously. Therefore, this study demonstrates the ability to improve Korean precipitation forecasts by optimizing microphysics in UM.

Optimizing conservation strategies for a threatened tree species: In situ conservation of white ash (Fraxinus americana L.) genetic diversity through insecticide treatment

Treesearch

Charles Flower; Jeremie Fant; Sean Hoban; Kathleen Knight; Laura Steger; Elijah Aubihl; Miquel Gonzalez-Meler; Stephen Forry; Andrea Hille; Alejandro Royo

2018-01-01

Forest resources face numerous threats that require costly management. Hence, there is an increasing need for data-informed strategies to guide conservation practices. The introduction of the emerald ash borer to North America has caused rapid declines in ash populations (Fraxinus spp. L.). Natural resource managers are faced with a choice of...

Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

NASA Astrophysics Data System (ADS)

Kim, Young Gyun; Lee, Jongsoo

2016-08-01

In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

NASA Astrophysics Data System (ADS)

Sheng, Lizeng

The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ˜ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

PubMed

Chen, Ying-ping; Chen, Chao-Hong

2010-01-01

An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

Optimal sensor placement for deployable antenna module health monitoring in SSPS using genetic algorithm

NASA Astrophysics Data System (ADS)

Yang, Chen; Zhang, Xuepan; Huang, Xiaoqi; Cheng, ZhengAi; Zhang, Xinghua; Hou, Xinbin

2017-11-01

The concept of space solar power satellite (SSPS) is an advanced system for collecting solar energy in space and transmitting it wirelessly to earth. However, due to the long service life, in-orbit damage may occur in the structural system of SSPS. Therefore, sensor placement layouts for structural health monitoring should be firstly considered in this concept. In this paper, based on genetic algorithm, an optimal sensor placement method for deployable antenna module health monitoring in SSPS is proposed. According to the characteristics of the deployable antenna module, the designs of sensor placement are listed. Furthermore, based on effective independence method and effective interval index, a combined fitness function is defined to maximize linear independence in targeted modes while simultaneously avoiding redundant information at nearby positions. In addition, by considering the reliability of sensors located at deployable mechanisms, another fitness function is constituted. Moreover, the solution process of optimal sensor placement by using genetic algorithm is clearly demonstrated. At last, a numerical example about the sensor placement layout in a deployable antenna module of SSPS is presented, which by synthetically considering all the above mentioned performances. All results can illustrate the effectiveness and feasibility of the proposed sensor placement method in SSPS.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

NASA Astrophysics Data System (ADS)

Sun, Xiuqiao; Wang, Jian

2018-07-01

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.

PubMed

Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

2015-04-17

This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors.

Genetic Algorithm (GA)-Based Inclinometer Layout Optimization

PubMed Central

Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

2015-01-01

This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500

Noise-Aided Logic in an Electronic Analog of Synthetic Genetic Networks

PubMed Central

Hellen, Edward H.; Dana, Syamal K.; Kurths, Jürgen; Kehler, Elizabeth; Sinha, Sudeshna

2013-01-01

We report the experimental verification of noise-enhanced logic behaviour in an electronic analog of a synthetic genetic network, composed of two repressors and two constitutive promoters. We observe good agreement between circuit measurements and numerical prediction, with the circuit allowing for robust logic operations in an optimal window of noise. Namely, the input-output characteristics of a logic gate is reproduced faithfully under moderate noise, which is a manifestation of the phenomenon known as Logical Stochastic Resonance. The two dynamical variables in the system yield complementary logic behaviour simultaneously. The system is easily morphed from AND/NAND to OR/NOR logic. PMID:24124531

A genetic algorithm used for solving one optimization problem

NASA Astrophysics Data System (ADS)

Shipacheva, E. N.; Petunin, A. A.; Berezin, I. M.

2017-12-01

A problem of minimizing the length of the blank run for a cutting tool during cutting of sheet materials into shaped blanks is discussed. This problem arises during the preparation of control programs for computerized numerical control (CNC) machines. A discrete model of the problem is analogous in setting to the generalized travelling salesman problem with limitations in the form of precursor conditions determined by the technological features of cutting. A certain variant of a genetic algorithm for solving this problem is described. The effect of the parameters of the developed algorithm on the solution result for the problem with limitations is investigated.

An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

PubMed Central

Wang, Jun; Zhou, Bihua; Zhou, Shudao

2016-01-01

This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874

Hybrid Genetic Agorithms and Line Search Method for Industrial Production Planning with Non-Linear Fitness Function

NASA Astrophysics Data System (ADS)

Vasant, Pandian; Barsoum, Nader

2008-10-01

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

A simulation-optimization model for Stone column-supported embankment stability considering rainfall effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deb, Kousik, E-mail: kousik@civil.iitkgp.ernet.in; Dhar, Anirban, E-mail: anirban@civil.iitkgp.ernet.in; Purohit, Sandip, E-mail: sandip.purohit91@gmail.com

Landslide due to rainfall has been and continues to be one of the most important concerns of geotechnical engineering. The paper presents the variation of factor of safety of stone column-supported embankment constructed over soft soil due to change in water level for an incessant period of rainfall. A combined simulation-optimization based methodology has been proposed to predict the critical surface of failure of the embankment and to optimize the corresponding factor of safety under rainfall conditions using an evolutionary genetic algorithm NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). It has been observed that the position of water table can be reliablymore » estimated with varying periods of infiltration using developed numerical method. The parametric study is presented to study the optimum factor of safety of the embankment and its corresponding critical failure surface under the steady-state infiltration condition. Results show that in case of floating stone columns, period of infiltration has no effect on factor of safety. Even critical failure surfaces for a particular floating column length remain same irrespective of rainfall duration.« less

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Optimal False Discovery Rate Control for Dependent Data

PubMed Central

Xie, Jichun; Cai, T. Tony; Maris, John; Li, Hongzhe

2013-01-01

This paper considers the problem of optimal false discovery rate control when the test statistics are dependent. An optimal joint oracle procedure, which minimizes the false non-discovery rate subject to a constraint on the false discovery rate is developed. A data-driven marginal plug-in procedure is then proposed to approximate the optimal joint procedure for multivariate normal data. It is shown that the marginal procedure is asymptotically optimal for multivariate normal data with a short-range dependent covariance structure. Numerical results show that the marginal procedure controls false discovery rate and leads to a smaller false non-discovery rate than several commonly used p-value based false discovery rate controlling methods. The procedure is illustrated by an application to a genome-wide association study of neuroblastoma and it identifies a few more genetic variants that are potentially associated with neuroblastoma than several p-value-based false discovery rate controlling procedures. PMID:23378870

Evolutionary Optimization of a Geometrically Refined Truss

NASA Technical Reports Server (NTRS)

Hull, P. V.; Tinker, M. L.; Dozier, G. V.

2007-01-01

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This Technical Publication (TP) presents a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: genetic algorithms and differential evolution to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this TP, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.

Communication strategies for enhancing understanding of the behavioral implications of genetic and biomarker tests for disease risk: the role of coherence.

PubMed

Cameron, Linda D; Marteau, Theresa M; Brown, Paul M; Klein, William M P; Sherman, Kerry A

2012-06-01

Individuals frequently have difficulty understanding how behavior can reduce genetically-conferred risk for diseases such as colon cancer. With increasing opportunities to purchase genetic tests, communication strategies are needed for presenting information in ways that optimize comprehension and adaptive behavior. Using the Common-Sense Model, we tested the efficacy of a strategy for providing information about the relationships (links) among the physiological processes underlying disease risk and protective action on understanding, protective action motivations, and willingness to purchase tests. We tested the generalizability of the strategy's effects across varying risk levels, for genetic tests versus tests of a non-genetic biomarker, and when using graphic and numeric risk formats. In an internet-based experiment, 749 adults from four countries responded to messages about a hypothetical test for colon cancer risk. Messages varied by Risk-Action Link Information (provision or no provision of information describing how a low-fat diet reduces risk given positive results, indicating presence of a gene fault), Risk Increment (20%, 50%, or 80% risk given positive results), Risk Format (numeric or graphic presentation of risk increments), and Test Type (genetic or enzyme). Providing risk-action link information enhanced beliefs of coherence (understanding how a low-fat diet reduces risk) and response efficacy (low-fat diets effectively reduce risk) and lowered appraisals of anticipated risk of colon cancer given positive results. These effects held across risk increments, risk formats, and test types. For genetic tests, provision of risk-action link information reduced the amount individuals were willing to pay for testing. Brief messages explaining how action can reduce genetic and biomarker-detected risks can promote beliefs motivating protective action. By enhancing understanding of behavioral control, they may reduce the perceived value of genetic risk information.

Numerical simulations of detonation propagation in gaseous fuel-air mixtures

NASA Astrophysics Data System (ADS)

Honhar, Praveen; Kaplan, Carolyn; Houim, Ryan; Oran, Elaine

2017-11-01

Unsteady multidimensional numerical simulations of detonation propagation and survival in mixtures of fuel (hydrogen or methane) diluted with air were carried out with a fully compressible Navier-Stokes solver using a simplified chemical-diffusive model (CDM). The CDM was derived using a genetic algorithm combined with the Nelder-Mead optimization algorithm and reproduces physically correct laminar flame and detonation properties. Cases studied are overdriven detonations propagating through confined mediums, with or without gradients in composition. Results from simulations confirm that the survival of the detonation depends on the channel heights. In addition, the simulations show that the propagation of the detonation waves depends on the steepness in composition gradients.

Numerical investigation on the viewing angle of a lenticular three-dimensional display with a triplet lens array.

PubMed

Kim, Hwi; Hahn, Joonku; Choi, Hee-Jin

2011-04-10

We investigate the viewing angle enhancement of a lenticular three-dimensional (3D) display with a triplet lens array. The theoretical limitations of the viewing angle and view number of the lenticular 3D display with the triplet lens array are analyzed numerically. For this, the genetic-algorithm-based design method of the triplet lens is developed. We show that a lenticular 3D display with viewing angle of 120° and 144 views without interview cross talk can be realized with the use of an optimally designed triplet lens array. © 2011 Optical Society of America

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

2005-01-01

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

A Gradient Taguchi Method for Engineering Optimization

NASA Astrophysics Data System (ADS)

Hwang, Shun-Fa; Wu, Jen-Chih; He, Rong-Song

2017-10-01

To balance the robustness and the convergence speed of optimization, a novel hybrid algorithm consisting of Taguchi method and the steepest descent method is proposed in this work. Taguchi method using orthogonal arrays could quickly find the optimum combination of the levels of various factors, even when the number of level and/or factor is quite large. This algorithm is applied to the inverse determination of elastic constants of three composite plates by combining numerical method and vibration testing. For these problems, the proposed algorithm could find better elastic constants in less computation cost. Therefore, the proposed algorithm has nice robustness and fast convergence speed as compared to some hybrid genetic algorithms.

Analysis of stock investment selection based on CAPM using covariance and genetic algorithm approach

NASA Astrophysics Data System (ADS)

Sukono; Susanti, D.; Najmia, M.; Lesmana, E.; Napitupulu, H.; Supian, S.; Putra, A. S.

2018-03-01

Investment is one of the economic growth factors of countries, especially in Indonesia. Stocks is a form of investment, which is liquid. In determining the stock investment decisions which need to be considered by investors is to choose stocks that can generate maximum returns with a minimum risk level. Therefore, we need to know how to allocate the capital which may give the optimal benefit. This study discusses the issue of stock investment based on CAPM which is estimated using covariance and Genetic Algorithm approach. It is assumed that the stocks analyzed follow the CAPM model. To do the estimation of beta parameter on CAPM equation is done by two approach, first is to be represented by covariance approach, and second with genetic algorithm optimization. As a numerical illustration, in this paper analyzed ten stocks traded on the capital market in Indonesia. The results of the analysis show that estimation of beta parameters using covariance and genetic algorithm approach, give the same decision, that is, six underpriced stocks with buying decision, and four overpriced stocks with a sales decision. Based on the analysis, it can be concluded that the results can be used as a consideration for investors buying six under-priced stocks, and selling four overpriced stocks.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

An Optimization Study of Hot Stamping Operation

NASA Astrophysics Data System (ADS)

Ghoo, Bonyoung; Umezu, Yasuyoshi; Watanabe, Yuko; Ma, Ninshu; Averill, Ron

2010-06-01

In the present study, 3-dimensional finite element analyses for hot-stamping processes of Audi B-pillar product are conducted using JSTAMP/NV and HEEDS. Special attention is paid to the optimization of simulation technology coupling with thermal-mechanical formulations. Numerical simulation based on FEM technology and optimization design using the hybrid adaptive SHERPA algorithm are applied to hot stamping operation to improve productivity. The robustness of the SHERPA algorithm is found through the results of the benchmark example. The SHERPA algorithm is shown to be far superior to the GA (Genetic Algorithm) in terms of efficiency, whose calculation time is about 7 times faster than that of the GA. The SHERPA algorithm could show high performance in a large scale problem having complicated design space and long calculation time.

Using genetic algorithm to solve a new multi-period stochastic optimization model

NASA Astrophysics Data System (ADS)

Zhang, Xin-Li; Zhang, Ke-Cun

2009-09-01

This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2006-01-01

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Genetic algorithms and their use in Geophysical Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, Paul B.

1999-04-01

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show thatmore » certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.« less

Genetic algorithms and their use in geophysical problems

NASA Astrophysics Data System (ADS)

Parker, Paul Bradley

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

Genetic algorithm to solve the problems of lectures and practicums scheduling

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

Optimization of gear ratio and power distribution for a multimotor powertrain of an electric vehicle

NASA Astrophysics Data System (ADS)

Urbina Coronado, Pedro Daniel; Orta Castañón, Pedro; Ahuett-Garza, Horacio

2018-02-01

The architecture and design of the propulsion system of electric vehicles are highly important for the reduction of energy losses. This work presents a powertrain composed of four electric motors in which each motor is connected with a different gear ratio to the differential of the rear axle. A strategy to reduce energy losses is proposed, in which two phases are applied. Phase 1 uses a divide-and-conquer approach to increase the overall output efficiency by obtaining the optimal torque distribution for the electric motors. Phase 2 applies a genetic algorithm to find the optimal value of the gear ratios, in which each individual of each generation applies Phase 1. The results show an optimized efficiency map for the output torque and speed of the powertrain. The increase in efficiency and the reduction of energy losses are validated by the use of numerical experiments in various driving cycles.

An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems

PubMed Central

Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun

2014-01-01

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940

Calibrating a Soil-Vegetation-Atmosphere system with a genetical algorithm

NASA Astrophysics Data System (ADS)

Schneider, S.; Jacques, D.; Mallants, D.

2009-04-01

Accuracy of model prediction is well known for being very sensitive to the quality of the calibration of the model. It is also known that quantifying soil hydraulic parameters in a Soil-Vegetation-Atmosphere (SVA) system is a highly non-linear parameter estimation problem, and that robust methods are needed to avoid the optimization process to lead to non-optimal parameters. Evolutionary algorithms and specifically genetic algorithms (GAs) are very well suited for those complex parameter optimization problems. The SVA system in this study concerns a pine stand on a heterogeneous sandy soil (podzol) in the north of Belgium (Campine region). Throughfall and other meteorological data and water contents at different soil depths have been recorded during one year at a daily time step. The water table level, which is varying between 95 and 170 cm, has been recorded with a frequency of 0.5 hours. Based on the profile description, four soil layers have been distinguished in the podzol and used for the numerical simulation with the hydrus1D model (Simunek and al., 2005). For the inversion procedure the MYGA program (Yedder, 2002), which is an elitism GA, was used. Optimization was based on the water content measurements realized at the depths of 10, 20, 40, 50, 60, 70, 90, 110, and 120 cm to estimate parameters describing the unsaturated hydraulic soil properties of the different soil layers. Comparison between the modeled and measured water contents shows a good similarity during the simulated year. Impacts of short and intensive events (rainfall) on the water content of the soil are also well reproduced. Errors on predictions are on average equal to 5%, which is considered as a good result. A. Ben Haj Yedder. Numerical optimization and optimal control : (molecular chemistry applications). PhD thesis, Ecole Nationale des Ponts et Chaussées, 2002. Šimůnek, J., M. Th. van Genuchten, and M. Šejna, The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably saturated media. Version 3.0, HYDRUS Software Series 1, Department of Environmental Sciences, University of California Riverside, Riverside, CA, 270 pp., 2005.

Global dynamic optimization approach to predict activation in metabolic pathways.

PubMed

de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

2014-01-06

During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

Systematic optimization of human pluripotent stem cells media using Design of Experiments

NASA Astrophysics Data System (ADS)

Marinho, Paulo A.; Chailangkarn, Thanathom; Muotri, Alysson R.

2015-05-01

Human pluripotent stem cells (hPSC) are used to study the early stages of human development in vitro and, increasingly due to somatic cell reprogramming, cellular and molecular mechanisms of disease. Cell culture medium is a critical factor for hPSC to maintain pluripotency and self-renewal. Numerous defined culture media have been empirically developed but never systematically optimized for culturing hPSC. We applied design of experiments (DOE), a powerful statistical tool, to improve the medium formulation for hPSC. Using pluripotency and cell growth as read-outs, we determined the optimal concentration of both basic fibroblast growth factor (bFGF) and neuregulin-1 beta 1 (NRG1β1). The resulting formulation, named iDEAL, improved the maintenance and passage of hPSC in both normal and stressful conditions, and affected trimethylated histone 3 lysine 27 (H3K27me3) epigenetic status after genetic reprogramming. It also enhances efficient hPSC plating as single cells. Altogether, iDEAL potentially allows scalable and controllable hPSC culture routine in translational research. Our DOE strategy could also be applied to hPSC differentiation protocols, which often require numerous and complex cell culture media.

Learning directed acyclic graphs from large-scale genomics data.

PubMed

Nikolay, Fabio; Pesavento, Marius; Kritikos, George; Typas, Nassos

2017-09-20

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal.

PubMed

Marrotte, R R; Gonzalez, A; Millien, V

2014-08-01

We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture-related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal. © 2014 John Wiley & Sons Ltd.

The role of noninvasive cardiovascular testing, applied clinical nutrition and nutritional supplements in the prevention and treatment of coronary heart disease.

PubMed

Houston, Mark

2018-03-01

Numerous clinical trials suggest that we have reached a limit in our ability to decrease the incidence of coronary heart disease (CHD) and cardiovascular disease (CVD) utilizing the traditional diagnostic evaluation, prevention and treatment strategies for the top five cardiovascular risk factors of hypertension, diabetes mellitus, dyslipidemia, obesity and smoking. About 80% of heart disease (heart attacks, angina, coronary heart disease and congestive heart failure) can be prevented by optimal nutrition, optimal exercise, optimal weight and body composition, mild alcohol intake and avoiding smoking. Statistics show that approximately 50% of patients continue to have CHD or myocardial infarction (MI) despite presently defined 'normal' levels of the five risk factors listed above. This is often referred to as the 'CHD gap'. Novel and more accurate definitions and evaluations of these top five risk factors are required, such as 24 h ambulatory blood pressure (ABM) results, advanced lipid profiles, redefined fasting and 2 h dysglycemia parameters, a focus on visceral obesity and body composition and the effects of adipokines on cardiovascular risk. There are numerous traumatic insults from the environment that damage the cardiovascular system but there are only three finite vascular endothelial responses, which are inflammation, oxidative stress and immune vascular dysfunction. In addition, the concept of translational cardiovascular medicine is mandatory in order to correlate the myriad of CHD risk factors to the presence or absence of functional or structural damage to the vascular system, preclinical and clinical CHD. This can be accomplished by utilizing advanced and updated CV risk scoring systems, new and redefined CV risk factors and biomarkers, micronutrient testing, cardiovascular genetics, nutrigenomics, metabolomics, genetic expression testing and noninvasive cardiovascular testing.

The role of noninvasive cardiovascular testing, applied clinical nutrition and nutritional supplements in the prevention and treatment of coronary heart disease

PubMed Central

Houston, Mark

2018-01-01

Numerous clinical trials suggest that we have reached a limit in our ability to decrease the incidence of coronary heart disease (CHD) and cardiovascular disease (CVD) utilizing the traditional diagnostic evaluation, prevention and treatment strategies for the top five cardiovascular risk factors of hypertension, diabetes mellitus, dyslipidemia, obesity and smoking. About 80% of heart disease (heart attacks, angina, coronary heart disease and congestive heart failure) can be prevented by optimal nutrition, optimal exercise, optimal weight and body composition, mild alcohol intake and avoiding smoking. Statistics show that approximately 50% of patients continue to have CHD or myocardial infarction (MI) despite presently defined ‘normal’ levels of the five risk factors listed above. This is often referred to as the ‘CHD gap’. Novel and more accurate definitions and evaluations of these top five risk factors are required, such as 24 h ambulatory blood pressure (ABM) results, advanced lipid profiles, redefined fasting and 2 h dysglycemia parameters, a focus on visceral obesity and body composition and the effects of adipokines on cardiovascular risk. There are numerous traumatic insults from the environment that damage the cardiovascular system but there are only three finite vascular endothelial responses, which are inflammation, oxidative stress and immune vascular dysfunction. In addition, the concept of translational cardiovascular medicine is mandatory in order to correlate the myriad of CHD risk factors to the presence or absence of functional or structural damage to the vascular system, preclinical and clinical CHD. This can be accomplished by utilizing advanced and updated CV risk scoring systems, new and redefined CV risk factors and biomarkers, micronutrient testing, cardiovascular genetics, nutrigenomics, metabolomics, genetic expression testing and noninvasive cardiovascular testing. PMID:29316855

"Genetically Engineered" Nanoelectronics

NASA Technical Reports Server (NTRS)

Klimeck, Gerhard; Salazar-Lazaro, Carlos H.; Stoica, Adrian; Cwik, Thomas

2000-01-01

The quantum mechanical functionality of nanoelectronic devices such as resonant tunneling diodes (RTDs), quantum well infrared-photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs) is enabled by material variations on an atomic scale. The design and optimization of such devices requires a fundamental understanding of electron transport in such dimensions. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool based on a fundamental non-equilibrium electron transport theory. NEW was combined with a parallelized genetic algorithm package (PGAPACK) to evolve structural and material parameters to match a desired set of experimental data. A numerical experiment that evolves structural variations such as layer widths and doping concentrations is performed to analyze an experimental current voltage characteristic. The genetic algorithm is found to drive the NEMO simulation parameters close to the experimentally prescribed layer thicknesses and doping profiles. With such a quantitative agreement between theory and experiment design synthesis can be performed.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Self-Tuning of Design Variables for Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Lin, Chaung; Juang, Jer-Nan

2000-01-01

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Derivation of Optimal Operating Rules for Large-scale Reservoir Systems Considering Multiple Trade-off

NASA Astrophysics Data System (ADS)

Zhang, J.; Lei, X.; Liu, P.; Wang, H.; Li, Z.

2017-12-01

Flood control operation of multi-reservoir systems such as parallel reservoirs and hybrid reservoirs often suffer from complex interactions and trade-off among tributaries and the mainstream. The optimization of such systems is computationally intensive due to nonlinear storage curves, numerous constraints and complex hydraulic connections. This paper aims to derive the optimal flood control operating rules based on the trade-off among tributaries and the mainstream using a new algorithm known as weighted non-dominated sorting genetic algorithm II (WNSGA II). WNSGA II could locate the Pareto frontier in non-dominated region efficiently due to the directed searching by weighted crowding distance, and the results are compared with those of conventional operating rules (COR) and single objective genetic algorithm (GA). Xijiang river basin in China is selected as a case study, with eight reservoirs and five flood control sections within four tributaries and the mainstream. Furthermore, the effects of inflow uncertainty have been assessed. Results indicate that: (1) WNSGA II could locate the non-dominated solutions faster and provide better Pareto frontier than the traditional non-dominated sorting genetic algorithm II (NSGA II) due to the weighted crowding distance; (2) WNSGA II outperforms COR and GA on flood control in the whole basin; (3) The multi-objective operating rules from WNSGA II deal with the inflow uncertainties better than COR. Therefore, the WNSGA II can be used to derive stable operating rules for large-scale reservoir systems effectively and efficiently.

Efficient calculation of higher-order optical waveguide dispersion.

PubMed

Mores, J A; Malheiros-Silveira, G N; Fragnito, H L; Hernández-Figueroa, H E

2010-09-13

An efficient numerical strategy to compute the higher-order dispersion parameters of optical waveguides is presented. For the first time to our knowledge, a systematic study of the errors involved in the higher-order dispersions' numerical calculation process is made, showing that the present strategy can accurately model those parameters. Such strategy combines a full-vectorial finite element modal solver and a proper finite difference differentiation algorithm. Its performance has been carefully assessed through the analysis of several key geometries. In addition, the optimization of those higher-order dispersion parameters can also be carried out by coupling to the present scheme a genetic algorithm, as shown here through the design of a photonic crystal fiber suitable for parametric amplification applications.

Semidefinite Relaxation-Based Optimization of Multiple-Input Wireless Power Transfer Systems

NASA Astrophysics Data System (ADS)

Lang, Hans-Dieter; Sarris, Costas D.

2017-11-01

An optimization procedure for multi-transmitter (MISO) wireless power transfer (WPT) systems based on tight semidefinite relaxation (SDR) is presented. This method ensures physical realizability of MISO WPT systems designed via convex optimization -- a robust, semi-analytical and intuitive route to optimizing such systems. To that end, the nonconvex constraints requiring that power is fed into rather than drawn from the system via all transmitter ports are incorporated in a convex semidefinite relaxation, which is efficiently and reliably solvable by dedicated algorithms. A test of the solution then confirms that this modified problem is equivalent (tight relaxation) to the original (nonconvex) one and that the true global optimum has been found. This is a clear advantage over global optimization methods (e.g. genetic algorithms), where convergence to the true global optimum cannot be ensured or tested. Discussions of numerical results yielded by both the closed-form expressions and the refined technique illustrate the importance and practicability of the new method. It, is shown that this technique offers a rigorous optimization framework for a broad range of current and emerging WPT applications.

Characterization and Optimization Design of the Polymer-Based Capacitive Micro-Arrayed Ultrasonic Transducer

NASA Astrophysics Data System (ADS)

Chiou, De-Yi; Chen, Mu-Yueh; Chang, Ming-Wei; Deng, Hsu-Cheng

2007-11-01

This study constructs an electromechanical finite element model of the polymer-based capacitive micro-arrayed ultrasonic transducer (P-CMUT). The electrostatic-structural coupled-field simulations are performed to investigate the operational characteristics, such as collapse voltage and resonant frequency. The numerical results are found to be in good agreement with experimental observations. The study of influence of each defined parameter on the collapse voltage and resonant frequency are also presented. To solve some conflict problems in diversely physical fields, an integrated design method is developed to optimize the geometric parameters of the P-CMUT. The optimization search routine conducted using the genetic algorithm (GA) is connected with the commercial FEM software ANSYS to obtain the best design variable using multi-objective functions. The results show that the optimal parameter values satisfy the conflicting objectives, namely to minimize the collapse voltage while simultaneously maintaining a customized frequency. Overall, the present result indicates that the combined FEM/GA optimization scheme provides an efficient and versatile approach of optimization design of the P-CMUT.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

An optimal modeling of multidimensional wave digital filtering network for free vibration analysis of symmetrically laminated composite FSDT plates

NASA Astrophysics Data System (ADS)

Tseng, Chien-Hsun

2015-02-01

The technique of multidimensional wave digital filtering (MDWDF) that builds on traveling wave formulation of lumped electrical elements, is successfully implemented on the study of dynamic responses of symmetrically laminated composite plate based on the first order shear deformation theory. The philosophy applied for the first time in this laminate mechanics relies on integration of certain principles involving modeling and simulation, circuit theory, and MD digital signal processing to provide a great variety of outstanding features. Especially benefited by the conservation of passivity gives rise to a nonlinear programming problem (NLP) for the issue of numerical stability of a MD discrete system. Adopting the augmented Lagrangian genetic algorithm, an effective optimization technique for rapidly achieving solution spaces of NLP models, numerical stability of the MDWDF network is well received at all time by the satisfaction of the Courant-Friedrichs-Levy stability criterion with the least restriction. In particular, optimum of the NLP has led to the optimality of the network in terms of effectively and accurately predicting the desired fundamental frequency, and thus to give an insight into the robustness of the network by looking at the distribution of system energies. To further explore the application of the optimum network, more numerical examples are engaged in efforts to achieve a qualitative understanding of the behavior of the laminar system. These are carried out by investigating various effects based on different stacking sequences, stiffness and span-to-thickness ratios, mode shapes and boundary conditions. Results are scrupulously validated by cross referencing with early published works, which show that the present method is in excellent agreement with other numerical and analytical methods.

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

NASA Astrophysics Data System (ADS)

Janardhanan, S.; Datta, B.

2011-12-01

Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin

2007-01-01

Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.

A multimodal logistics service network design with time windows and environmental concerns

PubMed Central

Zhang, Dezhi; He, Runzhong; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained. PMID:28934272

A multimodal logistics service network design with time windows and environmental concerns.

PubMed

Zhang, Dezhi; He, Runzhong; Li, Shuangyan; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

Optimizing the Long-Term Operating Plan of Railway Marshalling Station for Capacity Utilization Analysis

PubMed Central

Zhou, Wenliang; Yang, Xia; Deng, Lianbo

2014-01-01

Not only is the operating plan the basis of organizing marshalling station's operation, but it is also used to analyze in detail the capacity utilization of each facility in marshalling station. In this paper, a long-term operating plan is optimized mainly for capacity utilization analysis. Firstly, a model is developed to minimize railcars' average staying time with the constraints of minimum time intervals, marshalling track capacity, and so forth. Secondly, an algorithm is designed to solve this model based on genetic algorithm (GA) and simulation method. It divides the plan of whole planning horizon into many subplans, and optimizes them with GA one by one in order to obtain a satisfactory plan with less computing time. Finally, some numeric examples are constructed to analyze (1) the convergence of the algorithm, (2) the effect of some algorithm parameters, and (3) the influence of arrival train flow on the algorithm. PMID:25525614

Parametric Optimization of Thermoelectric Generators for Waste Heat Recovery

NASA Astrophysics Data System (ADS)

Huang, Shouyuan; Xu, Xianfan

2016-10-01

This paper presents a methodology for design optimization of thermoelectric-based waste heat recovery systems called thermoelectric generators (TEGs). The aim is to maximize the power output from thermoelectrics which are used as add-on modules to an existing gas-phase heat exchanger, without negative impacts, e.g., maintaining a minimum heat dissipation rate from the hot side. A numerical model is proposed for TEG coupled heat transfer and electrical power output. This finite-volume-based model simulates different types of heat exchangers, i.e., counter-flow and cross-flow, for TEGs. Multiple-filled skutterudites and bismuth-telluride-based thermoelectric modules (TEMs) are applied, respectively, in higher and lower temperature regions. The response surface methodology is implemented to determine the optimized TEG size along and across the flow direction and the height of thermoelectric couple legs, and to analyze their covariance and relative sensitivity. A genetic algorithm is employed to verify the globality of the optimum. The presented method will be generally useful for optimizing heat-exchanger-based TEG performance.

A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

PubMed

Dong, X; Zeng, S; Chen, J

2012-01-01

Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm

NASA Astrophysics Data System (ADS)

An, Zhao; Zhounian, Lai; Peng, Wu; Linlin, Cao; Dazhuan, Wu

2016-07-01

This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier-Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.

Designing train-speed trajectory with energy efficiency and service quality

NASA Astrophysics Data System (ADS)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

NASA Astrophysics Data System (ADS)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

NASA Astrophysics Data System (ADS)

Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

2016-03-01

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Modeling an aquatic ecosystem: application of an evolutionary algorithm with genetic doping to reduce prediction uncertainty

NASA Astrophysics Data System (ADS)

Friedel, Michael; Buscema, Massimo

2016-04-01

Aquatic ecosystem models can potentially be used to understand the influence of stresses on catchment resource quality. Given that catchment responses are functions of natural and anthropogenic stresses reflected in sparse and spatiotemporal biological, physical, and chemical measurements, an ecosystem is difficult to model using statistical or numerical methods. We propose an artificial adaptive systems approach to model ecosystems. First, an unsupervised machine-learning (ML) network is trained using the set of available sparse and disparate data variables. Second, an evolutionary algorithm with genetic doping is applied to reduce the number of ecosystem variables to an optimal set. Third, the optimal set of ecosystem variables is used to retrain the ML network. Fourth, a stochastic cross-validation approach is applied to quantify and compare the nonlinear uncertainty in selected predictions of the original and reduced models. Results are presented for aquatic ecosystems (tens of thousands of square kilometers) undergoing landscape change in the USA: Upper Illinois River Basin and Central Colorado Assessment Project Area, and Southland region, NZ.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling.

PubMed

Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

2017-01-01

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

PubMed Central

Deng, Qianwang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

2017-01-01

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed. PMID:28458687

Optimization of a pH-shift control strategy for producing monoclonal antibodies in Chinese hamster ovary cell cultures using a pH-dependent dynamic model.

PubMed

Hogiri, Tomoharu; Tamashima, Hiroshi; Nishizawa, Akitoshi; Okamoto, Masahiro

2018-02-01

To optimize monoclonal antibody (mAb) production in Chinese hamster ovary cell cultures, culture pH should be temporally controlled with high resolution. In this study, we propose a new pH-dependent dynamic model represented by simultaneous differential equations including a minimum of six system component, depending on pH value. All kinetic parameters in the dynamic model were estimated using an evolutionary numerical optimization (real-coded genetic algorithm) method based on experimental time-course data obtained at different pH values ranging from 6.6 to 7.2. We determined an optimal pH-shift schedule theoretically. We validated this optimal pH-shift schedule experimentally and mAb production increased by approximately 40% with this schedule. Throughout this study, it was suggested that the culture pH-shift optimization strategy using a pH-dependent dynamic model is suitable to optimize any pH-shift schedule for CHO cell lines used in mAb production projects. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Genetic Networks and Anticipation of Gene Expression Patterns

NASA Astrophysics Data System (ADS)

Gebert, J.; Lätsch, M.; Pickl, S. W.; Radde, N.; Weber, G.-W.; Wünschiers, R.

2004-08-01

An interesting problem for computational biology is the analysis of time-series expression data. Here, the application of modern methods from dynamical systems, optimization theory, numerical algorithms and the utilization of implicit discrete information lead to a deeper understanding. In [1], we suggested to represent the behavior of time-series gene expression patterns by a system of ordinary differential equations, which we analytically and algorithmically investigated under the parametrical aspect of stability or instability. Our algorithm strongly exploited combinatorial information. In this paper, we deepen, extend and exemplify this study from the viewpoint of underlying mathematical modelling. This modelling consists in evaluating DNA-microarray measurements as the basis of anticipatory prediction, in the choice of a smooth model given by differential equations, in an approach of the right-hand side with parametric matrices, and in a discrete approximation which is a least squares optimization problem. We give a mathematical and biological discussion, and pay attention to the special case of a linear system, where the matrices do not depend on the state of expressions. Here, we present first numerical examples.

A new numerical approach to solve Thomas-Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programming.

PubMed

Raja, Muhammad Asif Zahoor; Zameer, Aneela; Khan, Aziz Ullah; Wazwaz, Abdul Majid

2016-01-01

In this study, a novel bio-inspired computing approach is developed to analyze the dynamics of nonlinear singular Thomas-Fermi equation (TFE) arising in potential and charge density models of an atom by exploiting the strength of finite difference scheme (FDS) for discretization and optimization through genetic algorithms (GAs) hybrid with sequential quadratic programming. The FDS procedures are used to transform the TFE differential equations into a system of nonlinear equations. A fitness function is constructed based on the residual error of constituent equations in the mean square sense and is formulated as the minimization problem. Optimization of parameters for the system is carried out with GAs, used as a tool for viable global search integrated with SQP algorithm for rapid refinement of the results. The design scheme is applied to solve TFE for five different scenarios by taking various step sizes and different input intervals. Comparison of the proposed results with the state of the art numerical and analytical solutions reveals that the worth of our scheme in terms of accuracy and convergence. The reliability and effectiveness of the proposed scheme are validated through consistently getting optimal values of statistical performance indices calculated for a sufficiently large number of independent runs to establish its significance.

Nonlinear dynamic analysis and optimal trajectory planning of a high-speed macro-micro manipulator

NASA Astrophysics Data System (ADS)

Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Zhao, Xiao-wei

2017-09-01

This paper reports the nonlinear dynamic modeling and the optimal trajectory planning for a flexure-based macro-micro manipulator, which is dedicated to the large-scale and high-speed tasks. In particular, a macro- micro manipulator composed of a servo motor, a rigid arm and a compliant microgripper is focused. Moreover, both flexure hinges and flexible beams are considered. By combining the pseudorigid-body-model method, the assumed mode method and the Lagrange equation, the overall dynamic model is derived. Then, the rigid-flexible-coupling characteristics are analyzed by numerical simulations. After that, the microscopic scale vibration excited by the large-scale motion is reduced through the trajectory planning approach. Especially, a fitness function regards the comprehensive excitation torque of the compliant microgripper is proposed. The reference curve and the interpolation curve using the quintic polynomial trajectories are adopted. Afterwards, an improved genetic algorithm is used to identify the optimal trajectory by minimizing the fitness function. Finally, the numerical simulations and experiments validate the feasibility and the effectiveness of the established dynamic model and the trajectory planning approach. The amplitude of the residual vibration reduces approximately 54.9%, and the settling time decreases 57.1%. Therefore, the operation efficiency and manipulation stability are significantly improved.

Optimal placement of water-lubricated rubber bearings for vibration reduction of flexible multistage rotor systems

NASA Astrophysics Data System (ADS)

Liu, Shibing; Yang, Bingen

2017-10-01

Flexible multistage rotor systems with water-lubricated rubber bearings (WLRBs) have a variety of engineering applications. Filling a technical gap in the literature, this effort proposes a method of optimal bearing placement that minimizes the vibration amplitude of a WLRB-supported flexible rotor system with a minimum number of bearings. In the development, a new model of WLRBs and a distributed transfer function formulation are used to define a mixed continuous-and-discrete optimization problem. To deal with the case of uncertain number of WLRBs in rotor design, a virtual bearing method is devised. Solution of the optimization problem by a real-coded genetic algorithm yields the locations and lengths of water-lubricated rubber bearings, by which the prescribed operational requirements for the rotor system are satisfied. The proposed method is applicable either to preliminary design of a new rotor system with the number of bearings unforeknown or to redesign of an existing rotor system with a given number of bearings. Numerical examples show that the proposed optimal bearing placement is efficient, accurate and versatile in different design cases.

Scheduling Projects with Multiskill Learning Effect

PubMed Central

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost. PMID:24683355

Scheduling projects with multiskill learning effect.

PubMed

Zha, Hong; Zhang, Lianying

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost.

Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

PubMed Central

Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

PubMed

Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

Final Report: A Transport Phenomena Based Approach to Probe Evolution of Weld Macro and Microstructures and A Smart Bi-directional Model of Fusion Welding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dr. Tarasankar DebRoy

In recent years, applications of numerical heat transfer and fluid flow models of fusion welding have resulted in improved understanding of both the welding processes and welded materials. They have been used to accurately calculate thermal cycles and fusion zone geometry in many cases. Here we report the following three major advancements from this project. First, we show how microstructures, grain size distribution and topology of welds of several important engineering alloys can be computed starting from better understanding of the fusion welding process through numerical heat transfer and fluid flow calculations. Second, we provide a conclusive proof that themore » reliability of numerical heat transfer and fluid flow calculations can be significantly improved by optimizing several uncertain model parameters. Third, we demonstrate how the numerical heat transfer and fluid flow models can be combined with a suitable global optimization program such as a genetic algorithm for the tailoring of weld attributes such as attaining a specified weld geometry or a weld thermal cycle. The results of the project have been published in many papers and a listing of these are included together with a list of the graduate thesis that resulted from this project. The work supported by the DOE award has resulted in several important national and international awards. A listing of these awards and the status of the graduate students are also presented in this report.« less

Intelligent Space Tube Optimization for speeding ground water remedial design.

PubMed

Kalwij, Ineke M; Peralta, Richard C

2008-01-01

An innovative Intelligent Space Tube Optimization (ISTO) two-stage approach facilitates solving complex nonlinear flow and contaminant transport management problems. It reduces computational effort of designing optimal ground water remediation systems and strategies for an assumed set of wells. ISTO's stage 1 defines an adaptive mobile space tube that lengthens toward the optimal solution. The space tube has overlapping multidimensional subspaces. Stage 1 generates several strategies within the space tube, trains neural surrogate simulators (NSS) using the limited space tube data, and optimizes using an advanced genetic algorithm (AGA) with NSS. Stage 1 speeds evaluating assumed well locations and combinations. For a large complex plume of solvents and explosives, ISTO stage 1 reaches within 10% of the optimal solution 25% faster than an efficient AGA coupled with comprehensive tabu search (AGCT) does by itself. ISTO input parameters include space tube radius and number of strategies used to train NSS per cycle. Larger radii can speed convergence to optimality for optimizations that achieve it but might increase the number of optimizations reaching it. ISTO stage 2 automatically refines the NSS-AGA stage 1 optimal strategy using heuristic optimization (we used AGCT), without using NSS surrogates. Stage 2 explores the entire solution space. ISTO is applicable for many heuristic optimization settings in which the numerical simulator is computationally intensive, and one would like to reduce that burden.

Optimization of the magnetic horn for the nuSTORM non-conventional neutrino beam using the genetic algorithm

DOE PAGES

Liu, A.; Bross, A.; Neuffer, D.

2015-05-28

This paper describes the strategy for optimizing the magnetic horn for the neutrinos from STORed Muons (nuSTORM) facility. The nuSTORM magnetic horn is the primary collection device for the secondary particles generated by bombarding a solid target with 120 GeV protons. As a consequence of the non-conventional beamline designed for nuSTORM, the requirements on the horn are different from those for a conventional neutrino beamline. At nuSTORM, muons decay while circulating in the storage ring, and the detectors are placed downstream of the production straight so as to be exposed to the neutrinos from muon decay. nuSTORM aims at preciselymore » measuring the neutrino cross sections, and providing a definitive statement about the existence of sterile neutrinos. The nuSTORM horn aims at focusing the pions into a certain phase space so that more muons from pion decay can be accepted by the decay ring. The paper demonstrates a numerical method that was developed to optimize the horn design to gain higher neutrino flux from the circulating muons. A Genetic Algorithm (GA) was applied to the simultaneous optimization of the two objectives in this study. In conclusion, the application of the technique discussed in this paper is not limited to either the nuSTORM facility or muon based facilities, but can be used for other neutrino facilities that use magnetic horns as collection devices.« less

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

PubMed

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

PubMed Central

Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994

Multiple quay cranes scheduling for double cycling in container terminals

PubMed Central

Chu, Yanling; Zhang, Xiaoju; Yang, Zhongzhen

2017-01-01

Double cycling is an efficient tool to increase the efficiency of quay crane (QC) in container terminals. In this paper, an optimization model for double cycling is developed to optimize the operation sequence of multiple QCs. The objective is to minimize the makespan of the ship handling operation considering the ship balance constraint. To solve the model, an algorithm based on Lagrangian relaxation is designed. Finally, we compare the efficiency of the Lagrangian relaxation based heuristic with the branch-and-bound method and a genetic algorithm using instances of different sizes. The results of numerical experiments indicate that the proposed model can effectively reduce the unloading and loading times of QCs. The effects of the ship balance constraint are more notable when the number of QCs is high. PMID:28692699

Multiple quay cranes scheduling for double cycling in container terminals.

PubMed

Chu, Yanling; Zhang, Xiaoju; Yang, Zhongzhen

2017-01-01

Double cycling is an efficient tool to increase the efficiency of quay crane (QC) in container terminals. In this paper, an optimization model for double cycling is developed to optimize the operation sequence of multiple QCs. The objective is to minimize the makespan of the ship handling operation considering the ship balance constraint. To solve the model, an algorithm based on Lagrangian relaxation is designed. Finally, we compare the efficiency of the Lagrangian relaxation based heuristic with the branch-and-bound method and a genetic algorithm using instances of different sizes. The results of numerical experiments indicate that the proposed model can effectively reduce the unloading and loading times of QCs. The effects of the ship balance constraint are more notable when the number of QCs is high.

GMOtrack: generator of cost-effective GMO testing strategies.

PubMed

Novak, Petra Krau; Gruden, Kristina; Morisset, Dany; Lavrac, Nada; Stebih, Dejan; Rotter, Ana; Zel, Jana

2009-01-01

Commercialization of numerous genetically modified organisms (GMOs) has already been approved worldwide, and several additional GMOs are in the approval process. Many countries have adopted legislation to deal with GMO-related issues such as food safety, environmental concerns, and consumers' right of choice, making GMO traceability a necessity. The growing extent of GMO testing makes it important to study optimal GMO detection and identification strategies. This paper formally defines the problem of routine laboratory-level GMO tracking as a cost optimization problem, thus proposing a shift from "the same strategy for all samples" to "sample-centered GMO testing strategies." An algorithm (GMOtrack) for finding optimal two-phase (screening-identification) testing strategies is proposed. The advantages of cost optimization with increasing GMO presence on the market are demonstrated, showing that optimization approaches to analytic GMO traceability can result in major cost reductions. The optimal testing strategies are laboratory-dependent, as the costs depend on prior probabilities of local GMO presence, which are exemplified on food and feed samples. The proposed GMOtrack approach, publicly available under the terms of the General Public License, can be extended to other domains where complex testing is involved, such as safety and quality assurance in the food supply chain.

A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment.

PubMed

Li, Yanhui; Guo, Hao; Wang, Lin; Fu, Jing

2013-01-01

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

Genetic investigations on intracranial aneurysm: update and perspectives.

PubMed

Bourcier, Romain; Redon, Richard; Desal, Hubert

2015-04-01

Detection of an intracranial aneurysm (IA) is a common finding in MRI practice. Nowadays, the incidence of unruptured IA seems to be increasing with the continuous evolution of imaging techniques. Important modifiable risk factors for SAH are well defined, but familial history of IA is the best risk marker for the presence of IA. Numerous heritable conditions are associated with IA formation but these syndromes account for less than 1% of all IAs in the population. No diagnostic test based on genetic knowledge is currently available to identify theses mutations and patients who are at higher risk for developing IAs. In the longer term, a more comprehensive understanding of independent and interdependent molecular pathways germane to IA formation and rupture may guide the physician in developing targeted therapies and optimizing prognostic risk assessment. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Automated design of infrared digital metamaterials by genetic algorithm

NASA Astrophysics Data System (ADS)

Sugino, Yuya; Ishikawa, Atsushi; Hayashi, Yasuhiko; Tsuruta, Kenji

2017-08-01

We demonstrate automatic design of infrared (IR) metamaterials using a genetic algorithm (GA) and experimentally characterize their IR properties. To implement the automated design scheme of the metamaterial structures, we adopt a digital metamaterial consisting of 7 × 7 Au nano-pixels with an area of 200 nm × 200 nm, and their placements are coded as binary genes in the GA optimization process. The GA combined with three-dimensional (3D) finite element method (FEM) simulation is developed and applied to automatically construct a digital metamaterial to exhibit pronounced plasmonic resonances at the target IR frequencies. Based on the numerical results, the metamaterials are fabricated on a Si substrate over an area of 1 mm × 1 mm by using an EB lithography, Cr/Au (2/20 nm) depositions, and liftoff process. In the FT-IR measurement, pronounced plasmonic responses of each metamaterial are clearly observed near the targeted frequencies, although the synthesized pixel arrangements of the metamaterials are seemingly random. The corresponding numerical simulations reveal the important resonant behavior of each pixel and their hybridized systems. Our approach is fully computer-aided without artificial manipulation, thus paving the way toward the novel device design for next-generation plasmonic device applications.

Design and analysis of radiometric instruments using high-level numerical models and genetic algorithms

NASA Astrophysics Data System (ADS)

Sorensen, Ira Joseph

A primary objective of the effort reported here is to develop a radiometric instrument modeling environment to provide complete end-to-end numerical models of radiometric instruments, integrating the optical, electro-thermal, and electronic systems. The modeling environment consists of a Monte Carlo ray-trace (MCRT) model of the optical system coupled to a transient, three-dimensional finite-difference electrothermal model of the detector assembly with an analytic model of the signal-conditioning circuitry. The environment provides a complete simulation of the dynamic optical and electrothermal behavior of the instrument. The modeling environment is used to create an end-to-end model of the CERES scanning radiometer, and its performance is compared to the performance of an operational CERES total channel as a benchmark. A further objective of this effort is to formulate an efficient design environment for radiometric instruments. To this end, the modeling environment is then combined with evolutionary search algorithms known as genetic algorithms (GA's) to develop a methodology for optimal instrument design using high-level radiometric instrument models. GA's are applied to the design of the optical system and detector system separately and to both as an aggregate function with positive results.

Performance Improvement of a Return Channel in a Multistage Centrifugal Compressor Using Multiobjective Optimization.

PubMed

Nishida, Yoshifumi; Kobayashi, Hiromi; Nishida, Hideo; Sugimura, Kazuyuki

2013-05-01

The effect of the design parameters of a return channel on the performance of a multistage centrifugal compressor was numerically investigated, and the shape of the return channel was optimized using a multiobjective optimization method based on a genetic algorithm to improve the performance of the centrifugal compressor. The results of sensitivity analysis using Latin hypercube sampling suggested that the inlet-to-outlet area ratio of the return vane affected the total pressure loss in the return channel, and that the inlet-to-outlet radius ratio of the return vane affected the outlet flow angle from the return vane. Moreover, this analysis suggested that the number of return vanes affected both the loss and the flow angle at the outlet. As a result of optimization, the number of return vane was increased from 14 to 22 and the area ratio was decreased from 0.71 to 0.66. The radius ratio was also decreased from 2.1 to 2.0. Performance tests on a centrifugal compressor with two return channels (the original design and optimized design) were carried out using two-stage test apparatus. The measured flow distribution exhibited a swirl flow in the center region and a reversed swirl flow near the hub and shroud sides. The exit flow of the optimized design was more uniform than that of the original design. For the optimized design, the overall two-stage efficiency and pressure coefficient were increased by 0.7% and 1.5%, respectively. Moreover, the second-stage efficiency and pressure coefficient were respectively increased by 1.0% and 3.2%. It is considered that the increase in the second-stage efficiency was caused by the increased uniformity of the flow, and the rise in the pressure coefficient was caused by a decrease in the residual swirl flow. It was thus concluded from the numerical and experimental results that the optimized return channel improved the performance of the multistage centrifugal compressor.

An optimized resistor pattern for temperature gradient control in microfluidics

NASA Astrophysics Data System (ADS)

Selva, Bertrand; Marchalot, Julien; Jullien, Marie-Caroline

2009-06-01

In this paper, we demonstrate the possibility of generating high-temperature gradients with a linear temperature profile when heating is provided in situ. Thanks to improved optimization algorithms, the shape of resistors, which constitute the heating source, is optimized by applying the genetic algorithm NSGA-II (acronym for the non-dominated sorting genetic algorithm) (Deb et al 2002 IEEE Trans. Evol. Comput. 6 2). Experimental validation of the linear temperature profile within the cavity is carried out using a thermally sensitive fluorophore, called Rhodamine B (Ross et al 2001 Anal. Chem. 73 4117-23, Erickson et al 2003 Lab Chip 3 141-9). The high level of agreement obtained between experimental and numerical results serves to validate the accuracy of this method for generating highly controlled temperature profiles. In the field of actuation, such a device is of potential interest since it allows for controlling bubbles or droplets moving by means of thermocapillary effects (Baroud et al 2007 Phys. Rev. E 75 046302). Digital microfluidics is a critical area in the field of microfluidics (Dreyfus et al 2003 Phys. Rev. Lett. 90 14) as well as in the so-called lab-on-a-chip technology. Through an example, the large application potential of such a technique is demonstrated, which entails handling a single bubble driven along a cavity using simple and tunable embedded resistors.

Shape accuracy optimization for cable-rib tension deployable antenna structure with tensioned cables

NASA Astrophysics Data System (ADS)

Liu, Ruiwei; Guo, Hongwei; Liu, Rongqiang; Wang, Hongxiang; Tang, Dewei; Song, Xiaoke

2017-11-01

Shape accuracy is of substantial importance in deployable structures as the demand for large-scale deployable structures in various fields, especially in aerospace engineering, increases. The main purpose of this paper is to present a shape accuracy optimization method to find the optimal pretensions for the desired shape of cable-rib tension deployable antenna structure with tensioned cables. First, an analysis model of the deployable structure is established by using finite element method. In this model, geometrical nonlinearity is considered for the cable element and beam element. Flexible deformations of the deployable structure under the action of cable network and tensioned cables are subsequently analyzed separately. Moreover, the influence of pretension of tensioned cables on natural frequencies is studied. Based on the results, a genetic algorithm is used to find a set of reasonable pretension and thus minimize structural deformation under the first natural frequency constraint. Finally, numerical simulations are presented to analyze the deployable structure under two kinds of constraints. Results show that the shape accuracy and natural frequencies of deployable structure can be effectively improved by pretension optimization.

Advanced Covariance-Based Stochastic Inversion and Neuro-Genetic Optimization for Rosetta CONSERT Radar Data to Improve Spatial Resolution of Multi-Fractal Depth Profiles for Cometary Nucleus

NASA Astrophysics Data System (ADS)

Edenhofer, Peter; Ulamec, Stephan

2015-04-01

The paper is devoted to results of doctoral research work at University of Bochum as applied to the radar transmission experiment CONSERT of the ESA cometary mission Rosetta. This research aims at achieving the limits of optimum spatial (and temporal) resolution for radar remote sensing by implementation of covariance informations concerned with error-balanced control as well as coherence of wave propagation effects through random composite media involved (based on Joel Franklin's approach of extended stochastic inversion). As a consequence the well-known inherent numerical instabilities of remote sensing are significantly reduced in a robust way by increasing the weight of main diagonal elements of the resulting composite matrix to be inverted with respect to off-diagonal elements following synergy relations as to the principle of correlation receiver in wireless telecommunications. It is shown that the enhancement of resolution for remote sensing holds for an integral and differential equation approach of inversion as well. In addition to that the paper presents a discussion on how the efficiency of inversion for radar data gets achieved by an overall optimization of inversion due to a novel neuro-genetic approach. Such kind of approach is in synergy with the priority research program "Organic Computing" of DFG / German Research Organization. This Neuro-Genetic Optimization (NGO) turns out, firstly, to take into account more detailed physical informations supporting further improved resolution such as the process of accretion for cometary nucleus, wave propagation effects from rough surfaces, ground clutter, nonlinear focusing, etc. as well as, secondly, to accelerate the computing process of inversion in a really significantly enhanced and fast way, e.g., enabling online-control of autonomous processes such as detection of unknown objects, navigation, etc. The paper describes in some detail how this neuro-genetic approach of optimization is incorporated into the procedure of data inversion by combining inverted artificial neural networks of adequately chosen topology and learning routines for short access times with the concept of genetic algorithms enabling to achieve a multi-dimensional global optimum subject to a properly constructed and problem-oriented target function, ensemble selection rules, etc. Finally the paper discusses how the power of realistic simulation of the structures of the interior of a cometary nucleus can be improved by applying Benoit Mandelbrot's concept of fractal structures. It is shown how the fractal volumetric modelling of the nucleus of a comet can be accomplished by finite 3D elements of flexibility (serving topography and morphology as well) such as of tetrahedron shape with specific scaling factors of self similarity and a Maxwellian type of distribution function. By applying the widely accepted fBm-concept of fractal Brownian motion basically each of the corresponding Hurst exponents 0 (rough) < H < 1 (smooth) can be derived for the multi-fractal depth (and terrain) profiles of the equivalent dielectric constant per tomographic angular orbital segment of intersection by transmissive radar ray paths with the nucleus of the comet. Cooperative efforts and work are in progress to achieve numerical results of depth profiles for the nucleus of comet 67P/Churyumov-Gerasimenko.

Correlated noise-based switches and stochastic resonance in a bistable genetic regulation system

NASA Astrophysics Data System (ADS)

Wang, Can-Jun; Yang, Ke-Li

2016-07-01

The correlated noise-based switches and stochastic resonance are investigated in a bistable single gene switching system driven by an additive noise (environmental fluctuations), a multiplicative noise (fluctuations of the degradation rate). The correlation between the two noise sources originates from on the lysis-lysogeny pathway system of the λ phage. The steady state probability distribution is obtained by solving the time-independent Fokker-Planck equation, and the effects of noises are analyzed. The effects of noises on the switching time between the two stable states (mean first passage time) is investigated by the numerical simulation. The stochastic resonance phenomenon is analyzed by the power amplification factor. The results show that the multiplicative noise can induce the switching from "on" → "off" of the protein production, while the additive noise and the correlation between the noise sources can induce the inverse switching "off" → "on". A nonmonotonic behaviour of the average switching time versus the multiplicative noise intensity, for different cross-correlation and additive noise intensities, is observed in the genetic system. There exist optimal values of the additive noise, multiplicative noise and cross-correlation intensities for which the weak signal can be optimal amplified.

Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint.

PubMed

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-06

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location-routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

Optimization in fractional aircraft ownership

NASA Astrophysics Data System (ADS)

Septiani, R. D.; Pasaribu, H. M.; Soewono, E.; Fayalita, R. A.

2012-05-01

Fractional Aircraft Ownership is a new concept in flight ownership management system where each individual or corporation may own a fraction of an aircraft. In this system, the owners have privilege to schedule their flight according to their needs. Fractional management companies (FMC) manages all aspects of aircraft operations, including utilization of FMC's aircraft in combination of outsourced aircrafts. This gives the owners the right to enjoy the benefits of private aviations. However, FMC may have complicated business requirements that neither commercial airlines nor charter airlines faces. Here, optimization models are constructed to minimize the number of aircrafts in order to maximize the profit and to minimize the daily operating cost. In this paper, three kinds of demand scenarios are made to represent different flight operations from different types of fractional owners. The problems are formulated as an optimization of profit and a daily operational cost to find the optimum flight assignments satisfying the weekly and daily demand respectively from the owners. Numerical results are obtained by Genetic Algorithm method.

Calibrated Blade-Element/Momentum Theory Aerodynamic Model of the MARIN Stock Wind Turbine: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goupee, A.; Kimball, R.; de Ridder, E. J.

2015-04-02

In this paper, a calibrated blade-element/momentum theory aerodynamic model of the MARIN stock wind turbine is developed and documented. The model is created using open-source software and calibrated to closely emulate experimental data obtained by the DeepCwind Consortium using a genetic algorithm optimization routine. The provided model will be useful for those interested in validating interested in validating floating wind turbine numerical simulators that rely on experiments utilizing the MARIN stock wind turbine—for example, the International Energy Agency Wind Task 30’s Offshore Code Comparison Collaboration Continued, with Correlation project.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

NASA Astrophysics Data System (ADS)

Vasant, P.; Ganesan, T.; Elamvazuthi, I.

2012-11-01

A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

Optimization of wavefront coding imaging system using heuristic algorithms

NASA Astrophysics Data System (ADS)

González-Amador, E.; Padilla-Vivanco, A.; Toxqui-Quitl, C.; Zermeño-Loreto, O.

2017-08-01

Wavefront Coding (WFC) systems make use of an aspheric Phase-Mask (PM) and digital image processing to extend the Depth of Field (EDoF) of computational imaging systems. For years, several kinds of PM have been designed to produce a point spread function (PSF) near defocus-invariant. In this paper, the optimization of the phase deviation parameter is done by means of genetic algorithms (GAs). In this, the merit function minimizes the mean square error (MSE) between the diffraction limited Modulated Transfer Function (MTF) and the MTF of the system that is wavefront coded with different misfocus. WFC systems were simulated using the cubic, trefoil, and 4 Zernike polynomials phase-masks. Numerical results show defocus invariance aberration in all cases. Nevertheless, the best results are obtained by using the trefoil phase-mask, because the decoded image is almost free of artifacts.

Optimality models in the age of experimental evolution and genomics.

PubMed

Bull, J J; Wang, I-N

2010-09-01

Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well-researched organism allows dissection of the evolutionary process to identify causes of model failure--whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation--an especially useful augmentation to well-researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.

Interactive Genetic Algorithm - An Adaptive and Interactive Decision Support Framework for Design of Optimal Groundwater Monitoring Plans

NASA Astrophysics Data System (ADS)

Babbar-Sebens, M.; Minsker, B. S.

2006-12-01

In the water resources management field, decision making encompasses many kinds of engineering, social, and economic constraints and objectives. Representing all of these problem dependant criteria through models (analytical or numerical) and various formulations (e.g., objectives, constraints, etc.) within an optimization- simulation system can be a very non-trivial issue. Most models and formulations utilized for discerning desirable traits in a solution can only approximate the decision maker's (DM) true preference criteria, and they often fail to consider important qualitative and incomputable phenomena related to the management problem. In our research, we have proposed novel decision support frameworks that allow DMs to actively participate in the optimization process. The DMs explicitly indicate their true preferences based on their subjective criteria and the results of various simulation models and formulations. The feedback from the DMs is then used to guide the search process towards solutions that are "all-rounders" from the perspective of the DM. The two main research questions explored in this work are: a) Does interaction between the optimization algorithm and a DM assist the system in searching for groundwater monitoring designs that are robust from the DM's perspective?, and b) How can an interactive search process be made more effective when human factors, such as human fatigue and cognitive learning processes, affect the performance of the algorithm? The application of these frameworks on a real-world groundwater long-term monitoring (LTM) case study in Michigan highlighted the following salient advantages: a) in contrast to the non-interactive optimization methodology, the proposed interactive frameworks were able to identify low cost monitoring designs whose interpolation maps respected the expected spatial distribution of the contaminants, b) for many same-cost designs, the interactive methodologies were able to propose multiple alternatives that met the DM's preference criteria, therefore allowing the expert to select among several strong candidate designs depending on her/his LTM budget, c) two of the methodologies - Case-Based Micro Interactive Genetic Algorithm (CBMIGA) and Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) - were also able to assist in controlling human fatigue and adapt to the DM's learning process.

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Fast optimization of glide vehicle reentry trajectory based on genetic algorithm

NASA Astrophysics Data System (ADS)

Jia, Jun; Dong, Ruixing; Yuan, Xuejun; Wang, Chuangwei

2018-02-01

An optimization method of reentry trajectory based on genetic algorithm is presented to meet the need of reentry trajectory optimization for glide vehicle. The dynamic model for the glide vehicle during reentry period is established. Considering the constraints of heat flux, dynamic pressure, overload etc., the optimization of reentry trajectory is investigated by utilizing genetic algorithm. The simulation shows that the method presented by this paper is effective for the optimization of reentry trajectory of glide vehicle. The efficiency and speed of this method is comparative with the references. Optimization results meet all constraints, and the on-line fast optimization is potential by pre-processing the offline samples.

An approach for aerodynamic optimization of transonic fan blades

NASA Astrophysics Data System (ADS)

Khelghatibana, Maryam

Aerodynamic design optimization of transonic fan blades is a highly challenging problem due to the complexity of flow field inside the fan, the conflicting design requirements and the high-dimensional design space. In order to address all these challenges, an aerodynamic design optimization method is developed in this study. This method automates the design process by integrating a geometrical parameterization method, a CFD solver and numerical optimization methods that can be applied to both single and multi-point optimization design problems. A multi-level blade parameterization is employed to modify the blade geometry. Numerical analyses are performed by solving 3D RANS equations combined with SST turbulence model. Genetic algorithms and hybrid optimization methods are applied to solve the optimization problem. In order to verify the effectiveness and feasibility of the optimization method, a singlepoint optimization problem aiming to maximize design efficiency is formulated and applied to redesign a test case. However, transonic fan blade design is inherently a multi-faceted problem that deals with several objectives such as efficiency, stall margin, and choke margin. The proposed multi-point optimization method in the current study is formulated as a bi-objective problem to maximize design and near-stall efficiencies while maintaining the required design pressure ratio. Enhancing these objectives significantly deteriorate the choke margin, specifically at high rotational speeds. Therefore, another constraint is embedded in the optimization problem in order to prevent the reduction of choke margin at high speeds. Since capturing stall inception is numerically very expensive, stall margin has not been considered as an objective in the problem statement. However, improving near-stall efficiency results in a better performance at stall condition, which could enhance the stall margin. An investigation is therefore performed on the Pareto-optimal solutions to demonstrate the relation between near-stall efficiency and stall margin. The proposed method is applied to redesign NASA rotor 67 for single and multiple operating conditions. The single-point design optimization showed +0.28 points improvement of isentropic efficiency at design point, while the design pressure ratio and mass flow are, respectively, within 0.12% and 0.11% of the reference blade. Two cases of multi-point optimization are performed: First, the proposed multi-point optimization problem is relaxed by removing the choke margin constraint in order to demonstrate the relation between near-stall efficiency and stall margin. An investigation on the Pareto-optimal solutions of this optimization shows that the stall margin has been increased with improving near-stall efficiency. The second multi-point optimization case is performed with considering all the objectives and constraints. One selected optimized design on the Pareto front presents +0.41, +0.56 and +0.9 points improvement in near-peak efficiency, near-stall efficiency and stall margin, respectively. The design pressure ratio and mass flow are, respectively, within 0.3% and 0.26% of the reference blade. Moreover the optimized design maintains the required choking margin. Detailed aerodynamic analyses are performed to investigate the effect of shape optimization on shock occurrence, secondary flows, tip leakage and shock/tip-leakage interactions in both single and multi-point optimizations.

A method of network topology optimization design considering application process characteristic

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo

2018-03-01

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

Aero-thermal optimization of film cooling flow parameters on the suction surface of a high pressure turbine blade

NASA Astrophysics Data System (ADS)

El Ayoubi, Carole; Hassan, Ibrahim; Ghaly, Wahid

2012-11-01

This paper aims to optimize film coolant flow parameters on the suction surface of a high-pressure gas turbine blade in order to obtain an optimum compromise between a superior cooling performance and a minimum aerodynamic penalty. An optimization algorithm coupled with three-dimensional Reynolds-averaged Navier Stokes analysis is used to determine the optimum film cooling configuration. The VKI blade with two staggered rows of axially oriented, conically flared, film cooling holes on its suction surface is considered. Two design variables are selected; the coolant to mainstream temperature ratio and total pressure ratio. The optimization objective consists of maximizing the spatially averaged film cooling effectiveness and minimizing the aerodynamic penalty produced by film cooling. The effect of varying the coolant flow parameters on the film cooling effectiveness and the aerodynamic loss is analyzed using an optimization method and three dimensional steady CFD simulations. The optimization process consists of a genetic algorithm and a response surface approximation of the artificial neural network type to provide low-fidelity predictions of the objective function. The CFD simulations are performed using the commercial software CFX. The numerical predictions of the aero-thermal performance is validated against a well-established experimental database.

A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

PubMed Central

Guo, Hao; Fu, Jing

2013-01-01

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment. PMID:24489489

The synthesis paradigm in genetics.

PubMed

Rice, William R

2014-02-01

Experimental genetics with model organisms and mathematically explicit genetic theory are generally considered to be the major paradigms by which progress in genetics is achieved. Here I argue that this view is incomplete and that pivotal advances in genetics--and other fields of biology--are also made by synthesizing disparate threads of extant information rather than generating new information from experiments or formal theory. Because of the explosive expansion of information in numerous "-omics" data banks, and the fragmentation of genetics into numerous subdisciplines, the importance of the synthesis paradigm will likely expand with time.

Fuel management optimization using genetic algorithms and expert knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1996-09-01

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

Comparison of genetic algorithm methods for fuel management optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-12-31

The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.

Sensitivity of blackbody effective emissivity to wavelength and temperature: By genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ejigu, E. K.; Liedberg, H. G.

A variable-temperature blackbody (VTBB) is used to calibrate an infrared radiation thermometer (pyrometer). The effective emissivity (ε{sub eff}) of a VTBB is dependent on temperature and wavelength other than the geometry of the VTBB. In the calibration process the effective emissivity is often assumed to be constant within the wavelength and temperature range. There are practical situations where the sensitivity of the effective emissivity needs to be known and correction has to be applied. We present a method using a genetic algorithm to investigate the sensitivity of the effective emissivity to wavelength and temperature variation. Two matlab® programs are generated:more » the first to model the radiance temperature calculation and the second to connect the model to the genetic algorithm optimization toolbox. The effective emissivity parameter is taken as a chromosome and optimized at each wavelength and temperature point. The difference between the contact temperature (reading from a platinum resistance thermometer or liquid in glass thermometer) and radiance temperature (calculated from the ε{sub eff} values) is used as an objective function where merit values are calculated and best fit ε{sub eff} values selected. The best fit ε{sub eff} values obtained as a solution show how sensitive they are to temperature and wavelength parameter variation. Uncertainty components that arise from wavelength and temperature variation are determined based on the sensitivity analysis. Numerical examples are considered for illustration.« less

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem.

PubMed

Contreras-Bolton, Carlos; Parada, Victor

2015-01-01

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

PubMed Central

2015-01-01

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature. PMID:26367182

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Enhanced entrainability of genetic oscillators by period mismatch

PubMed Central

Hasegawa, Yoshihiko; Arita, Masanori

2013-01-01

Biological oscillators coordinate individual cellular components so that they function coherently and collectively. They are typically composed of multiple feedback loops, and period mismatch is unavoidable in biological implementations. We investigated the advantageous effect of this period mismatch in terms of a synchronization response to external stimuli. Specifically, we considered two fundamental models of genetic circuits: smooth and relaxation oscillators. Using phase reduction and Floquet multipliers, we numerically analysed their entrainability under different coupling strengths and period ratios. We found that a period mismatch induces better entrainment in both types of oscillator; the enhancement occurs in the vicinity of the bifurcation on their limit cycles. In the smooth oscillator, the optimal period ratio for the enhancement coincides with the experimentally observed ratio, which suggests biological exploitation of the period mismatch. Although the origin of multiple feedback loops is often explained as a passive mechanism to ensure robustness against perturbation, we study the active benefits of the period mismatch, which include increasing the efficiency of the genetic oscillators. Our findings show a qualitatively different perspective for both the inherent advantages of multiple loops and their essentiality. PMID:23389900

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

PubMed Central

Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

2015-01-01

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

The Glass is Half Full and Half Empty: A population-representative twin study testing if Optimism and Pessimism are distinct systems

PubMed Central

Bates, Timothy C.

2015-01-01

Optimism and pessimism are associated with important outcomes including health and depression. Yet it is unclear if these apparent polar opposites form a single dimension or reflect two distinct systems. The extent to which personality accounts for differences in optimism/pessimism is also controversial. Here, we addressed these questions in a genetically informative sample of 852 pairs of twins. Distinct genetic influences on optimism and pessimism were found. Significant family-level environment effects also emerged, accounting for much of the negative relationship between optimism and pessimism, as well as a link to neuroticism. A general positive genetics factor exerted significant links among both personality and life-orientation traits. Both optimism bias and pessimism also showed genetic variance distinct from all effects of personality, and from each other. PMID:26561494

Causes of individual differences in adolescent optimism: a study in Dutch twins and their siblings.

PubMed

Mavioğlu, Rezan Nehir; Boomsma, Dorret I; Bartels, Meike

2015-11-01

The aim of this study was to investigate the degree to which genetic and environmental influences affect variation in adolescent optimism. Optimism (3 items and 6 items approach) and pessimism were assessed by the Life Orientation Test-Revised (LOT-R) in 5,187 adolescent twins and 999 of their non-twin siblings from the Netherlands Twin Register (NTR). Males reported significantly higher optimism scores than females, while females score higher on pessimism. Genetic structural equation modeling revealed that about one-third of the variance in optimism and pessimism was due to additive genetic effects, with the remaining variance being explained by non-shared environmental effects. A bivariate correlated factor model revealed two dimensions with a genetic correlation of -.57 (CI -.67, -.47), while the non-shared environmental correlation was estimated to be -.21 (CI -.25, -.16). Neither an effect of shared environment, non-additive genetic influences, nor quantitative sex differences was found for both dimensions. This result indicates that individual differences in adolescent optimism are mainly accounted for by non-shared environmental factors. These environmental factors do not contribute to the similarity of family members, but to differences between them. Familial resemblance in optimism and pessimism assessed in adolescents is fully accounted for by genetic overlap between family members.

Partial discharge localization in power transformers based on the sequential quadratic programming-genetic algorithm adopting acoustic emission techniques

NASA Astrophysics Data System (ADS)

Liu, Hua-Long; Liu, Hua-Dong

2014-10-01

Partial discharge (PD) in power transformers is one of the prime reasons resulting in insulation degradation and power faults. Hence, it is of great importance to study the techniques of the detection and localization of PD in theory and practice. The detection and localization of PD employing acoustic emission (AE) techniques, as a kind of non-destructive testing, plus due to the advantages of powerful capability of locating and high precision, have been paid more and more attention. The localization algorithm is the key factor to decide the localization accuracy in AE localization of PD. Many kinds of localization algorithms exist for the PD source localization adopting AE techniques including intelligent and non-intelligent algorithms. However, the existed algorithms possess some defects such as the premature convergence phenomenon, poor local optimization ability and unsuitability for the field applications. To overcome the poor local optimization ability and easily caused premature convergence phenomenon of the fundamental genetic algorithm (GA), a new kind of improved GA is proposed, namely the sequence quadratic programming-genetic algorithm (SQP-GA). For the hybrid optimization algorithm, SQP-GA, the sequence quadratic programming (SQP) algorithm which is used as a basic operator is integrated into the fundamental GA, so the local searching ability of the fundamental GA is improved effectively and the premature convergence phenomenon is overcome. Experimental results of the numerical simulations of benchmark functions show that the hybrid optimization algorithm, SQP-GA, is better than the fundamental GA in the convergence speed and optimization precision, and the proposed algorithm in this paper has outstanding optimization effect. At the same time, the presented SQP-GA in the paper is applied to solve the ultrasonic localization problem of PD in transformers, then the ultrasonic localization method of PD in transformers based on the SQP-GA is proposed. And localization results based on the SQP-GA are compared with some algorithms such as the GA, some other intelligent and non-intelligent algorithms. The results of calculating examples both stimulated and spot experiments demonstrate that the localization method based on the SQP-GA can effectively prevent the results from getting trapped into the local optimum values, and the localization method is of great feasibility and very suitable for the field applications, and the precision of localization is enhanced, and the effectiveness of localization is ideal and satisfactory.

LEO cooperative multi-spacecraft refueling mission optimization considering J2 perturbation and target's surplus propellant constraint

NASA Astrophysics Data System (ADS)

Zhao, Zhao; Zhang, Jin; Li, Hai-yang; Zhou, Jian-yong

2017-01-01

The optimization of an LEO cooperative multi-spacecraft refueling mission considering the J2 perturbation and target's surplus propellant constraint is studied in the paper. First, a mission scenario is introduced. One service spacecraft and several target spacecraft run on an LEO near-circular orbit, the service spacecraft rendezvouses with some service positions one by one, and target spacecraft transfer to corresponding service positions respectively. Each target spacecraft returns to its original position after obtaining required propellant and the service spacecraft returns to its original position after refueling all target spacecraft. Next, an optimization model of this mission is built. The service sequence, orbital transfer time, and service position are used as deign variables, whereas the propellant cost is used as the design objective. The J2 perturbation, time constraint and the target spacecraft's surplus propellant capability constraint are taken into account. Then, a hybrid two-level optimization approach is presented to solve the formulated mixed integer nonlinear programming (MINLP) problem. A hybrid-encoding genetic algorithm is adopted to seek the near optimal solution in the up-level optimization, while a linear relative dynamic equation considering the J2 perturbation is used to obtain the impulses of orbital transfer in the low-level optimization. Finally, the effectiveness of the proposed model and method is validated by numerical examples.

A hybrid optimization approach in non-isothermal glass molding

NASA Astrophysics Data System (ADS)

Vu, Anh-Tuan; Kreilkamp, Holger; Krishnamoorthi, Bharathwaj Janaki; Dambon, Olaf; Klocke, Fritz

2016-10-01

Intensively growing demands on complex yet low-cost precision glass optics from the today's photonic market motivate the development of an efficient and economically viable manufacturing technology for complex shaped optics. Against the state-of-the-art replication-based methods, Non-isothermal Glass Molding turns out to be a promising innovative technology for cost-efficient manufacturing because of increased mold lifetime, less energy consumption and high throughput from a fast process chain. However, the selection of parameters for the molding process usually requires a huge effort to satisfy precious requirements of the molded optics and to avoid negative effects on the expensive tool molds. Therefore, to reduce experimental work at the beginning, a coupling CFD/FEM numerical modeling was developed to study the molding process. This research focuses on the development of a hybrid optimization approach in Non-isothermal glass molding. To this end, an optimal configuration with two optimization stages for multiple quality characteristics of the glass optics is addressed. The hybrid Back-Propagation Neural Network (BPNN)-Genetic Algorithm (GA) is first carried out to realize the optimal process parameters and the stability of the process. The second stage continues with the optimization of glass preform using those optimal parameters to guarantee the accuracy of the molded optics. Experiments are performed to evaluate the effectiveness and feasibility of the model for the process development in Non-isothermal glass molding.

Statistics of optimal information flow in ensembles of regulatory motifs

NASA Astrophysics Data System (ADS)

Crisanti, Andrea; De Martino, Andrea; Fiorentino, Jonathan

2018-02-01

Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N , (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.

Two-machine flow shop scheduling integrated with preventive maintenance planning

NASA Astrophysics Data System (ADS)

Wang, Shijin; Liu, Ming

2016-02-01

This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.

Inverse problem of the vibrational band gap of periodically supported beam

NASA Astrophysics Data System (ADS)

Shi, Xiaona; Shu, Haisheng; Dong, Fuzhen; Zhao, Lei

2017-04-01

The researches of periodic structures have a long history with the main contents confined in the field of forward problem. In this paper, the inverse problem is considered and an overall frame is proposed which includes two main stages, i.e., the band gap criterion and its optimization. As a preliminary investigation, the inverse problem of the flexural vibrational band gap of a periodically supported beam is analyzed. According to existing knowledge of its forward problem, the band gap criterion is given in implicit form. Then, two cases with three independent parameters, namely the double supported case and the triple one, are studied in detail and the explicit expressions of the feasible domain are constructed by numerical fitting. Finally, the parameter optimization of the double supported case with three variables is conducted using genetic algorithm aiming for the best mean attenuation within specified frequency band.

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Shah, Rahul H.

Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.

Neuro-evolutionary computing paradigm for Painlevé equation-II in nonlinear optics

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Ahmad, Sufyan; Awais, Muhammad; Ul Islam Ahmad, Siraj; Asif Zahoor Raja, Muhammad

2018-05-01

The aim of this study is to investigate the numerical treatment of the Painlevé equation-II arising in physical models of nonlinear optics through artificial intelligence procedures by incorporating a single layer structure of neural networks optimized with genetic algorithms, sequential quadratic programming and active set techniques. We constructed a mathematical model for the nonlinear Painlevé equation-II with the help of networks by defining an error-based cost function in mean square sense. The performance of the proposed technique is validated through statistical analyses by means of the one-way ANOVA test conducted on a dataset generated by a large number of independent runs.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Transonic Wing Shape Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

2002-01-01

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

Aerodynamic design using numerical optimization

NASA Technical Reports Server (NTRS)

Murman, E. M.; Chapman, G. T.

1983-01-01

The procedure of using numerical optimization methods coupled with computational fluid dynamic (CFD) codes for the development of an aerodynamic design is examined. Several approaches that replace wind tunnel tests, develop pressure distributions and derive designs, or fulfill preset design criteria are presented. The method of Aerodynamic Design by Numerical Optimization (ADNO) is described and illustrated with examples.

Optimizing ion channel models using a parallel genetic algorithm on graphical processors.

PubMed

Ben-Shalom, Roy; Aviv, Amit; Razon, Benjamin; Korngreen, Alon

2012-01-01

We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs. Copyright © 2012 Elsevier B.V. All rights reserved.

Policy tree optimization for adaptive management of water resources systems

NASA Astrophysics Data System (ADS)

Herman, Jonathan; Giuliani, Matteo

2017-04-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points" that suggest the need of updating the policy. However, there remains a need for a general method to optimize the choice of the signposts to be used and their threshold values. This work contributes a general framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. Given a set of feature variables (e.g., reservoir level, inflow observations, inflow forecasts), the resulting policy defines both the optimal reservoir operations and the conditions under which such operations should be triggered. We demonstrate the approach using Folsom Reservoir (California) as a case study, in which operating policies must balance the risk of both floods and droughts. Numerical results show that the tree-based policies outperform the ones designed via Dynamic Programming. In addition, they display good adaptive capacity to the changing climate, successfully adapting the reservoir operations across a large set of uncertain climate scenarios.

Genetic algorithm in the structural design of Cooke triplet lenses

NASA Astrophysics Data System (ADS)

Hazra, Lakshminarayan; Banerjee, Saswatee

1999-08-01

This paper is in tune with our efforts to develop a systematic method for multicomponent lens design. Our aim is to find a suitable starting point in the final configuration space, so that popular local search methods like damped least squares (DLS) may directly lead to a useful solution. For 'ab initio' design problems, a thin lens layout specifying the powers of the individual components and the intercomponent separations are worked out analytically. Requirements of central aberration targets for the individual components in order to satisfy the prespecified primary aberration targets for the overall system are then determined by nonlinear optimization. The next step involves structural design of the individual components by optimization techniques. This general method may be adapted for the design of triplets and their derivatives. However, for the thin lens design of a Cooke triplet composed of three airspaced singlets, the two steps of optimization mentioned above may be combined into a single optimization procedure. The optimum configuration for each of the single set, catering to the required Gaussian specification and primary aberration targets for the Cooke triplet, are determined by an application of genetic algorithm (GA). Our implementation of this algorithm is based on simulations of some complex tools of natural evolution, like selection, crossover and mutation. Our version of GA may or may not converge to a unique optimum, depending on some of the algorithm specific parameter values. With our algorithm, practically useful solutions are always available, although convergence to a global optimum can not be guaranteed. This is perfectly in keeping with our need to allow 'floating' of aberration targets in the subproblem level. Some numerical results dealing with our preliminary investigations on this problem are presented.

Beyond Agar: Gel Substrates with Improved Optical Clarity and Drug Efficiency and Reduced Autofluorescence for Microbial Growth Experiments

PubMed Central

McElfresh, Cameron; Wong, Lily R.

2015-01-01

Agar, a seaweed extract, has been the standard support matrix for microbial experiments for over a century. Recent developments in high-throughput genetic screens have created a need to reevaluate the suitability of agar for use as colony support, as modern robotic printing systems now routinely spot thousands of colonies within the area of a single microtiter plate. Identifying optimal biophysical, biochemical, and biological properties of the gel support matrix in these extreme experimental conditions is instrumental to achieving the best possible reproducibility and sensitivity. Here we systematically evaluate a range of gelling agents by using the yeast Saccharomyces cerevisiae as a model microbe. We find that carrageenan and Phytagel have superior optical clarity and reduced autofluorescence, crucial for high-resolution imaging and fluorescent reporter screens. Nutrient choice and use of refined Noble agar or pure agarose reduce the effective dose of numerous selective drugs by >50%, potentially enabling large cost savings in genetic screens. Using thousands of mutant yeast strains to compare colony growth between substrates, we found no evidence of significant growth or nutrient biases between gel substrates, indicating that researchers could freely pick and choose the optimal gel for their respective application and experimental condition. PMID:26070672

Beyond Agar: Gel Substrates with Improved Optical Clarity and Drug Efficiency and Reduced Autofluorescence for Microbial Growth Experiments.

PubMed

Jaeger, Philipp A; McElfresh, Cameron; Wong, Lily R; Ideker, Trey

2015-08-15

Agar, a seaweed extract, has been the standard support matrix for microbial experiments for over a century. Recent developments in high-throughput genetic screens have created a need to reevaluate the suitability of agar for use as colony support, as modern robotic printing systems now routinely spot thousands of colonies within the area of a single microtiter plate. Identifying optimal biophysical, biochemical, and biological properties of the gel support matrix in these extreme experimental conditions is instrumental to achieving the best possible reproducibility and sensitivity. Here we systematically evaluate a range of gelling agents by using the yeast Saccharomyces cerevisiae as a model microbe. We find that carrageenan and Phytagel have superior optical clarity and reduced autofluorescence, crucial for high-resolution imaging and fluorescent reporter screens. Nutrient choice and use of refined Noble agar or pure agarose reduce the effective dose of numerous selective drugs by >50%, potentially enabling large cost savings in genetic screens. Using thousands of mutant yeast strains to compare colony growth between substrates, we found no evidence of significant growth or nutrient biases between gel substrates, indicating that researchers could freely pick and choose the optimal gel for their respective application and experimental condition. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm

NASA Astrophysics Data System (ADS)

Mahdavi, Seyed Hossein; Razak, Hashim Abdul

2016-06-01

This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.

New approaches to optimization in aerospace conceptual design

NASA Technical Reports Server (NTRS)

Gage, Peter J.

1995-01-01

Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

NASA Astrophysics Data System (ADS)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.

Multi-Item Multiperiodic Inventory Control Problem with Variable Demand and Discounts: A Particle Swarm Optimization Algorithm

PubMed Central

Mousavi, Seyed Mohsen; Niaki, S. T. A.; Bahreininejad, Ardeshir; Musa, Siti Nurmaya

2014-01-01

A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA. PMID:25093195

Optimal placement of tuning masses on truss structures by genetic algorithms

NASA Technical Reports Server (NTRS)

Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.

1993-01-01

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

A Novel Protocol for Model Calibration in Biological Wastewater Treatment

PubMed Central

Zhu, Ao; Guo, Jianhua; Ni, Bing-Jie; Wang, Shuying; Yang, Qing; Peng, Yongzhen

2015-01-01

Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models. PMID:25682959

Microfluidics: a transformational tool for nanomedicine development and production.

PubMed

Garg, Shyam; Heuck, Gesine; Ip, Shell; Ramsay, Euan

2016-11-01

Microfluidic devices are mircoscale fluidic circuits used to manipulate liquids at the nanoliter scale. The ability to control the mixing of fluids and the continuous nature of the process make it apt for solvent/antisolvent precipitation of drug-delivery nanoparticles. This review describes the use of numerous microfluidic designs for the formulation and production of lipid nanoparticles, liposomes and polymer nanoparticles to encapsulate and deliver small molecule or genetic payloads. The advantages of microfluidics are illustrated through examples from literature comparing conventional processes such as beaker and T-tube mixing to microfluidic approaches. Particular emphasis is placed on examples of microfluidic nanoparticle formulations that have been tested in vitro and in vivo. Fine control of process parameters afforded by microfluidics, allows unprecedented optimization of nanoparticle quality and encapsulation efficiency. Automation improves the reproducibility and optimization of formulations. Furthermore, the continuous nature of the microfluidic process is inherently scalable, allowing optimization at low volumes, which is advantageous with scarce or costly materials, as well as scale-up through process parallelization. Given these advantages, microfluidics is poised to become the new paradigm for nanomedicine formulation and production.

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

PubMed Central

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-01

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network. PMID:29316639

Evolutionary Optimization of Quadrifilar Helical and Yagi-Uda Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Stoica, Adrian; Clancy, Daniel (Technical Monitor)

2002-01-01

We present optimization results obtained for two type of antennas using evolutionary algorithms. A quadrifilar helical UHF antenna is currently flying aboard NASA's Mars Odyssey spacecraft and is due to reach final Martian orbit insertion in January, 2002. Using this antenna as a benchmark, we ran experiments employing a coevolutionary genetic algorithm to evolve a quadrifilar helical design in-situ - i.e., in the presence of a surrounding structure. Results show a 93% improvement at 400 MHz and a 48% improvement at 438 MHz in the average gain. The evolved antenna is also one-fourth the size. Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain and the inclusion of numerous parasitic elements. Our fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain.

A universal TagModule collection for parallel genetic analysis of microorganisms

PubMed Central

Oh, Julia; Fung, Eula; Price, Morgan N.; Dehal, Paramvir S.; Davis, Ronald W.; Giaever, Guri; Nislow, Corey; Arkin, Adam P.; Deutschbauer, Adam

2010-01-01

Systems-level analyses of non-model microorganisms are limited by the existence of numerous uncharacterized genes and a corresponding over-reliance on automated computational annotations. One solution to this challenge is to disrupt gene function using DNA tag technology, which has been highly successful in parallelizing reverse genetics in Saccharomyces cerevisiae and has led to discoveries in gene function, genetic interactions and drug mechanism of action. To extend the yeast DNA tag methodology to a wide variety of microorganisms and applications, we have created a universal, sequence-verified TagModule collection. A hallmark of the 4280 TagModules is that they are cloned into a Gateway entry vector, thus facilitating rapid transfer to any compatible genetic system. Here, we describe the application of the TagModules to rapidly generate tagged mutants by transposon mutagenesis in the metal-reducing bacterium Shewanella oneidensis MR-1 and the pathogenic yeast Candida albicans. Our results demonstrate the optimal hybridization properties of the TagModule collection, the flexibility in applying the strategy to diverse microorganisms and the biological insights that can be gained from fitness profiling tagged mutant collections. The publicly available TagModule collection is a platform-independent resource for the functional genomics of a wide range of microbial systems in the post-genome era. PMID:20494978

Scheduling structural health monitoring activities for optimizing life-cycle costs and reliability of wind turbines

NASA Astrophysics Data System (ADS)

Hanish Nithin, Anu; Omenzetter, Piotr

2017-04-01

Optimization of the life-cycle costs and reliability of offshore wind turbines (OWTs) is an area of immense interest due to the widespread increase in wind power generation across the world. Most of the existing studies have used structural reliability and the Bayesian pre-posterior analysis for optimization. This paper proposes an extension to the previous approaches in a framework for probabilistic optimization of the total life-cycle costs and reliability of OWTs by combining the elements of structural reliability/risk analysis (SRA), the Bayesian pre-posterior analysis with optimization through a genetic algorithm (GA). The SRA techniques are adopted to compute the probabilities of damage occurrence and failure associated with the deterioration model. The probabilities are used in the decision tree and are updated using the Bayesian analysis. The output of this framework would determine the optimal structural health monitoring and maintenance schedules to be implemented during the life span of OWTs while maintaining a trade-off between the life-cycle costs and risk of the structural failure. Numerical illustrations with a generic deterioration model for one monitoring exercise in the life cycle of a system are demonstrated. Two case scenarios, namely to build initially an expensive and robust or a cheaper but more quickly deteriorating structures and to adopt expensive monitoring system, are presented to aid in the decision-making process.

Optimization of Wind Turbine Airfoils/Blades and Wind Farm Layouts

NASA Astrophysics Data System (ADS)

Chen, Xiaomin

Shape optimization is widely used in the design of wind turbine blades. In this dissertation, a numerical optimization method called Genetic Algorithm (GA) is applied to address the shape optimization of wind turbine airfoils and blades. In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. The FX, DU and NACA 64 series airfoils are thick airfoils widely used for wind turbine blade application. They have several advantages in meeting the intrinsic requirements for wind turbines in terms of design point, off-design capabilities and structural properties. This research employ both single- and multi-objective genetic algorithms (SOGA and MOGA) for shape optimization of Flatback, FX, DU and NACA 64 series airfoils to achieve maximum lift and/or maximum lift to drag ratio. The commercially available software FLUENT is employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a two-equation Shear Stress Transport (SST) turbulence model and a three equation k-kl-o turbulence model. The optimization methodology is validated by an optimization study of subsonic and transonic airfoils (NACA0012 and RAE 2822 airfoils). In this dissertation, we employ DU 91-W2-250, FX 66-S196-V1, NACA 64421, and Flat-back series of airfoils (FB-3500-0050, FB-3500-0875, and FB-3500-1750) and compare their performance with S809 airfoil used in NREL Phase II and III wind turbines; the lift and drag coefficient data for these airfoils sections are available. The output power of the turbine is calculated using these airfoil section blades for a given B and lambda and is compared with the original NREL Phase II and Phase III turbines using S809 airfoil section. It is shown that by a suitable choice of airfoil section of HAWT blade, the power generated by the turbine can be significantly increased. Parametric studies are also conducted by varying the turbine diameter. In addition, a simplified dynamic inflow model is integrated into the BEM theory. It is shown that the improved BEM theory has superior performance in capturing the instantaneous behavior of wind turbines due to the existence of wind turbine wake or temporal variations in wind velocity. The dissertation also considers the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal --Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally position the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed. Finally, some preliminary investigation of shape optimization of 3D wind turbine blades at low Reynolds numbers is conducted. The optimization employs a 3D straight untapered wind turbine blade with cross section of NACA 0012 airfoils as the geometry of baseline blade. The optimization objective is to achieve maximum Cl/Cd as well as maximum Cl. The multi-objective genetic algorithm is employed together with the commercially available software FLUENT for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a one-equation Sparlart-Allmaras turbulence model. The results show excellent performance of the optimized wind turbine blade and indicate the feasibility of optimization on real wind turbine blades with more complex shapes in the future. (Abstract shortened by UMI.)

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Benford, Andrew; Tinker, Michael L.

2004-01-01

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

Superscattering of light optimized by a genetic algorithm

NASA Astrophysics Data System (ADS)

Mirzaei, Ali; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.

2014-07-01

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2002-01-01

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

Never Use the Complete Search Space: a Concept to Enhance the Optimization Procedure for Monitoring Networks

NASA Astrophysics Data System (ADS)

Bode, F.; Reuschen, S.; Nowak, W.

2015-12-01

Drinking-water well catchments include many potential sources of contaminations like gas stations or agriculture. Finding optimal positions of early-warning monitoring wells is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The overall goal of this project is to develop and establish a concept to assess, design and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: a high detection probability, which can be reached by maximizing the "field of vision" of the monitoring network, a long early-warning time such that there is enough time left to install counter measures after first detection, and the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, scenario analyses for real data, respectively, wrapped up within the framework of formal multi-objective optimization using a genetic algorithm.In order to speed up the optimization process and to better explore the Pareto-front, we developed a concept that forces the algorithm to search only in regions of the search space where promising solutions can be expected. We are going to show how to define these regions beforehand, using knowledge of the optimization problem, but also how to define them independently of problem attributes. With that, our method can be used with and/or without detailed knowledge of the objective functions.In summary, our study helps to improve optimization results in less optimization time by meaningful restrictions of the search space. These restrictions can be done independently of the optimization problem, but also in a problem-specific manner.

Application of Shuffled Frog Leaping Algorithm and Genetic Algorithm for the Optimization of Urban Stormwater Drainage

NASA Astrophysics Data System (ADS)

Kumar, S.; Kaushal, D. R.; Gosain, A. K.

2017-12-01

Urban hydrology will have an increasing role to play in the sustainability of human settlements. Expansion of urban areas brings significant changes in physical characteristics of landuse. Problems with administration of urban flooding have their roots in concentration of population within a relatively small area. As watersheds are urbanized, infiltration decreases, pattern of surface runoff is changed generating high peak flows, large runoff volumes from urban areas. Conceptual rainfall-runoff models have become a foremost tool for predicting surface runoff and flood forecasting. Manual calibration is often time consuming and tedious because of the involved subjectivity, which makes automatic approach more preferable. The calibration of parameters usually includes numerous criteria for evaluating the performances with respect to the observed data. Moreover, derivation of objective function assosciat6ed with the calibration of model parameters is quite challenging. Various studies dealing with optimization methods has steered the embracement of evolution based optimization algorithms. In this paper, a systematic comparison of two evolutionary approaches to multi-objective optimization namely shuffled frog leaping algorithm (SFLA) and genetic algorithms (GA) is done. SFLA is a cooperative search metaphor, stimulated by natural memetics based on the population while, GA is based on principle of survival of the fittest and natural evolution. SFLA and GA has been employed for optimizing the major parameters i.e. width, imperviousness, Manning's coefficient and depression storage for the highly urbanized catchment of Delhi, India. The study summarizes the auto-tuning of a widely used storm water management model (SWMM), by internal coupling of SWMM with SFLA and GA separately. The values of statistical parameters such as, Nash-Sutcliffe efficiency (NSE) and Percent Bias (PBIAS) were found to lie within the acceptable limit, indicating reasonably good model performance. Overall, this study proved promising for assessing risk in urban drainage systems and should prove useful to improve integrity of the urban system, its reliability and provides guidance for inundation preparedness.Keywords: Hydrologic model, SWMM, Urbanization, SFLA and GA.

Using genetic algorithms to optimize the analogue method for precipitation prediction in the Swiss Alps

NASA Astrophysics Data System (ADS)

Horton, Pascal; Jaboyedoff, Michel; Obled, Charles

2018-01-01

Analogue methods provide a statistical precipitation prediction based on synoptic predictors supplied by general circulation models or numerical weather prediction models. The method samples a selection of days in the archives that are similar to the target day to be predicted, and consider their set of corresponding observed precipitation (the predictand) as the conditional distribution for the target day. The relationship between the predictors and predictands relies on some parameters that characterize how and where the similarity between two atmospheric situations is defined. This relationship is usually established by a semi-automatic sequential procedure that has strong limitations: (i) it cannot automatically choose the pressure levels and temporal windows (hour of the day) for a given meteorological variable, (ii) it cannot handle dependencies between parameters, and (iii) it cannot easily handle new degrees of freedom. In this work, a global optimization approach relying on genetic algorithms could optimize all parameters jointly and automatically. The global optimization was applied to some variants of the analogue method for the Rhône catchment in the Swiss Alps. The performance scores increased compared to reference methods, especially for days with high precipitation totals. The resulting parameters were found to be relevant and coherent between the different subregions of the catchment. Moreover, they were obtained automatically and objectively, which reduces the effort that needs to be invested in exploration attempts when adapting the method to a new region or for a new predictand. For example, it obviates the need to assess a large number of combinations of pressure levels and temporal windows of predictor variables that were manually selected beforehand. The optimization could also take into account parameter inter-dependencies. In addition, the approach allowed for new degrees of freedom, such as a possible weighting between pressure levels, and non-overlapping spatial windows.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Air data system optimization using a genetic algorithm

NASA Technical Reports Server (NTRS)

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2001-01-01

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

An extended continuum model considering optimal velocity change with memory and numerical tests

NASA Astrophysics Data System (ADS)

Qingtao, Zhai; Hongxia, Ge; Rongjun, Cheng

2018-01-01

In this paper, an extended continuum model of traffic flow is proposed with the consideration of optimal velocity changes with memory. The new model's stability condition and KdV-Burgers equation considering the optimal velocities change with memory are deduced through linear stability theory and nonlinear analysis, respectively. Numerical simulation is carried out to study the extended continuum model, which explores how optimal velocity changes with memory affected velocity, density and energy consumption. Numerical results show that when considering the effects of optimal velocity changes with memory, the traffic jams can be suppressed efficiently. Both the memory step and sensitivity parameters of optimal velocity changes with memory will enhance the stability of traffic flow efficiently. Furthermore, numerical results demonstrates that the effect of optimal velocity changes with memory can avoid the disadvantage of historical information, which increases the stability of traffic flow on road, and so it improve the traffic flow stability and minimize cars' energy consumptions.

The human genetic history of the Americas: the final frontier.

PubMed

O'Rourke, Dennis H; Raff, Jennifer A

2010-02-23

The Americas, the last continents to be entered by modern humans, were colonized during the late Pleistocene via a land bridge across what is now the Bering strait. However, the timing and nature of the initial colonization events remain contentious. The Asian origin of the earliest Americans has been amply established by numerous classical marker studies of the mid-twentieth century. More recently, mtDNA sequences, Y-chromosome and autosomal marker studies have provided a higher level of resolution in confirming the Asian origin of indigenous Americans and provided more precise time estimates for the emergence of Native Americans. But these data raise many additional questions regarding source populations, number and size of colonizing groups and the points of entry to the Americas. Rapidly accumulating molecular data from populations throughout the Americas, increased use of demographic models to test alternative colonization scenarios, and evaluation of the concordance of archaeological, paleoenvironmental and genetic data provide optimism for a fuller understanding of the initial colonization of the Americas. Copyright 2010 Elsevier Ltd. All rights reserved.

Genetic Engineering of the Phosphocarrier Protein NPr of the Escherichia coli Phosphotransferase System Selectively Improves Sugar Uptake Activity*

PubMed Central

Lopez-de los Santos, Yossef; Chan, Henry; Cantu, Vito A.; Rettner, Rachael; Sanchez, Filiberto; Zhang, Zhongge; Saier, Milton H.; Soberon, Xavier

2012-01-01

The Escherichia coli phosphoenolpyruvate:sugar phosphotransferase system (PTS) in prokaryotes mediates the uptake and phosphorylation of its numerous substrates through a phosphoryl transfer chain where a phosphoryl transfer protein, HPr, transfers its phosphoryl group to any of several sugar-specific Enzyme IIA proteins in preparation for sugar transport. A phosphoryl transfer protein of the PTS, NPr, homologous to HPr, functions to regulate nitrogen metabolism and shows virtually no enzymatic cross-reactivity with HPr. Here we describe the genetic engineering of a “chimeric” HPr/NPr protein, termed CPr14 because 14 amino acid residues of the interface were replaced. CPr14 shows decreased activity with most PTS permeases relative to HPr, but increases activity with the broad specificity mannose permease. The results lead to the proposal that HPr is not optimal for most PTS permeases but instead represents a compromise with suboptimal activity for most PTS permeases. The evolutionary implications are discussed. PMID:22767600

Comparing ESC and iPSC-Based Models for Human Genetic Disorders.

PubMed

Halevy, Tomer; Urbach, Achia

2014-10-24

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients' somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn't be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.

Metabolic engineering of Cyanobacteria and microalgae for enhanced production of biofuels and high-value products.

PubMed

Gomaa, M A; Al-Haj, L; Abed, R M M

2016-10-01

A lot of research has been performed on Cyanobacteria and microalgae with the aim to produce numerous biotechnological products. However, native strains have a few shortcomings, like limitations in cultivation, harvesting and product extraction, which prevents reaching optimal production value at lowest costs. Such limitations require the intervention of genetic engineering to produce strains with superior properties. Promising advancements in the cultivation of Cyanobacteria and microalgae have been achieved by improving photosynthetic efficiency through increasing RuBisCO activity and truncation of light-harvesting antennae. Genetic engineering has also contributed to final product extraction by inducing autolysis and product secretory systems, to enable direct product recovery without going through costly extraction steps. In this review, we summarize the different enzymes and pathways that have been targeted thus far for improving cultivation aspects, harvesting and product extraction in Cyanobacteria and microalgae. With synthetic biology advancements, genetically engineered strains can be generated to resolve demanding process issues and achieve economic practicality. This comprehensive overview of gene modifications will be useful to researchers in the field to employ on their strains to increase their yields and improve the economic feasibility of the production process. © 2016 The Society for Applied Microbiology.

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

EPA Science Inventory

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

Determination of Cole-Cole parameters using only the real part of electrical impedivity measurements.

PubMed

Miranda, David A; Rivera, S A López

2008-05-01

An algorithm is presented to determine the Cole-Cole parameters of electrical impedivity using only measurements of its real part. The algorithm is based on two multi-fold direct inversion methods for the Cole-Cole and Debye equations, respectively, and a genetic algorithm for the optimization of the mean square error between experimental and calculated data. The algorithm has been developed to obtain the Cole-Cole parameters from experimental data, which were used to screen cervical intra-epithelial neoplasia. The proposed algorithm was compared with different numerical integrations of the Kramers-Kronig relation and the result shows that this algorithm is the best. A high immunity to noise was obtained.

Particle Swarm Optimization Toolbox

NASA Technical Reports Server (NTRS)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Numerical solution to generalized Burgers'-Fisher equation using Exp-function method hybridized with heuristic computation.

PubMed

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.

Numerical Solution to Generalized Burgers'-Fisher Equation Using Exp-Function Method Hybridized with Heuristic Computation

PubMed Central

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

NASA Astrophysics Data System (ADS)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Analytical optimal pulse shapes obtained with the aid of genetic algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guerrero, Rubén D., E-mail: rdguerrerom@unal.edu.co; Arango, Carlos A.; Reyes, Andrés

2015-09-28

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding themore » interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.« less

Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

NASA Astrophysics Data System (ADS)

Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius

2018-02-01

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

Problem solving with genetic algorithms and Splicer

NASA Technical Reports Server (NTRS)

Bayer, Steven E.; Wang, Lui

1991-01-01

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

PubMed

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Xiaobiao; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Optimization of selection for growth in Menz Sheep while minimizing inbreeding depression in fitness traits

PubMed Central

2013-01-01

The genetic trends in fitness (inbreeding, fertility and survival) of a closed nucleus flock of Menz sheep under selection during ten years for increased body weight were investigated to evaluate the consequences of selection for body weight on fitness. A mate selection tool was used to optimize in retrospect the actual selection and matings conducted over the project period to assess if the observed genetic gains in body weight could have been achieved with a reduced level of inbreeding. In the actual selection, the genetic trends for yearling weight, fertility of ewes and survival of lambs were 0.81 kg, –0.00026% and 0.016% per generation. The average inbreeding coefficient remained zero for the first few generations and then tended to increase over generations. The genetic gains achieved with the optimized retrospective selection and matings were highly comparable with the observed values, the correlation between the average breeding values of lambs born from the actual and optimized matings over the years being 0.99. However, the level of inbreeding with the optimized mate selections remained zero until late in the years of selection. Our results suggest that an optimal selection strategy that considers both genetic merits and coancestry of mates should be adopted to sustain the Menz sheep breeding program. PMID:23783076

Weight optimization of plane truss using genetic algorithm

NASA Astrophysics Data System (ADS)

Neeraja, D.; Kamireddy, Thejesh; Santosh Kumar, Potnuru; Simha Reddy, Vijay

2017-11-01

Optimization of structure on basis of weight has many practical benefits in every engineering field. The efficiency is proportionally related to its weight and hence weight optimization gains prime importance. Considering the field of civil engineering, weight optimized structural elements are economical and easier to transport to the site. In this study, genetic optimization algorithm for weight optimization of steel truss considering its shape, size and topology aspects has been developed in MATLAB. Material strength and Buckling stability have been adopted from IS 800-2007 code of construction steel. The constraints considered in the present study are fabrication, basic nodes, displacements, and compatibility. Genetic programming is a natural selection search technique intended to combine good solutions to a problem from many generations to improve the results. All solutions are generated randomly and represented individually by a binary string with similarities of natural chromosomes, and hence it is termed as genetic programming. The outcome of the study is a MATLAB program, which can optimise a steel truss and display the optimised topology along with element shapes, deflections, and stress results.

Optimization of fuels from waste composition with application of genetic algorithm.

PubMed

Małgorzata, Wzorek

2014-05-01

The objective of this article is to elaborate a method to optimize the composition of the fuels from sewage sludge (PBS fuel - fuel based on sewage sludge and coal slime, PBM fuel - fuel based on sewage sludge and meat and bone meal, PBT fuel - fuel based on sewage sludge and sawdust). As a tool for an optimization procedure, the use of a genetic algorithm is proposed. The optimization task involves the maximization of mass fraction of sewage sludge in a fuel developed on the basis of quality-based criteria for the use as an alternative fuel used by the cement industry. The selection criteria of fuels composition concerned such parameters as: calorific value, content of chlorine, sulphur and heavy metals. Mathematical descriptions of fuel compositions and general forms of the genetic algorithm, as well as the obtained optimization results are presented. The results of this study indicate that the proposed genetic algorithm offers an optimization tool, which could be useful in the determination of the composition of fuels that are produced from waste.

Optimizing luminescent solar concentrator design

DOE PAGES

Hernandez-Noyola, Hermilo; Potterveld, David H.; Holt, Roy J.; ...

2011-12-21

Luminescent Solar Concentrators (LSCs) use fluorescent materials and light guides to convert direct and diffuse sunlight into concentrated wavelength-shifted light that produces electrical power in small photovoltaic (PV) cells with the goal of significantly reducing the cost of solar energy utilization. In this paper we present an optimization analysis based on the implementation of a genetic algorithm (GA) subroutine to a numerical ray-tracing Monte Carlo model of an LSC, SIMSOLAR-P. The initial use of the GA implementation in SIMSOLAR-P is to find the optimal parameters of a hypothetical ‘‘perfect luminescent material’’ that obeys the Kennard Stepanov (K-S) thermodynamic relationship betweenmore » emission and absorption. The optimization balances the efficiency losses in the wavelength shift and PV conversion with the efficiency losses due to re-scattering of light out of the collector. The theoretical limits of efficiency are provided for one, two and three layer configurations; the results show that a single layer configuration is far from optimal and adding a second layer in the LSC with wavelength shifted material in the near infrared region significantly increases the power output, while the gain in power by adding a third layer is relatively small. Here, the results of this study provide a theoretical upper limit to the performance of an LSC and give guidance for the properties required for luminescent materials, such as quantum nanocrystals, to operate efficiently in planar LSC configurations« less

Stochastic Set-Based Particle Swarm Optimization Based on Local Exploration for Solving the Carpool Service Problem.

PubMed

Chou, Sheng-Kai; Jiau, Ming-Kai; Huang, Shih-Chia

2016-08-01

The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP.

Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model

PubMed Central

Fei, Ze-yuan; Xu, Yi-feng; Wang, Jie; Fan, Bing-feng; Ma, Xue-jin; Wang, Gang

2018-01-01

Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality. PMID:29515883

Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model.

PubMed

Li, Jian; Fei, Ze-Yuan; Xu, Yi-Feng; Wang, Jie; Fan, Bing-Feng; Ma, Xue-Jin; Wang, Gang

2018-02-01

Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.

Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model

NASA Astrophysics Data System (ADS)

Li, Jian; Fei, Ze-yuan; Xu, Yi-feng; Wang, Jie; Fan, Bing-feng; Ma, Xue-jin; Wang, Gang

2018-02-01

Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow

NASA Astrophysics Data System (ADS)

Peralta, Richard C.; Forghani, Ali; Fayad, Hala

2014-04-01

Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.

New Results in Astrodynamics Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

1998-01-01

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

Case Study on Optimal Routing in Logistics Network by Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Wang, Xiaoguang; Lin, Lin; Gen, Mitsuo; Shiota, Mitsushige

Recently, research on logistics caught more and more attention. One of the important issues on logistics system is to find optimal delivery routes with the least cost for products delivery. Numerous models have been developed for that reason. However, due to the diversity and complexity of practical problem, the existing models are usually not very satisfying to find the solution efficiently and convinently. In this paper, we treat a real-world logistics case with a company named ABC Co. ltd., in Kitakyusyu Japan. Firstly, based on the natures of this conveyance routing problem, as an extension of transportation problem (TP) and fixed charge transportation problem (fcTP) we formulate the problem as a minimum cost flow (MCF) model. Due to the complexity of fcTP, we proposed a priority-based genetic algorithm (pGA) approach to find the most acceptable solution to this problem. In this pGA approach, a two-stage path decoding method is adopted to develop delivery paths from a chromosome. We also apply the pGA approach to this problem, and compare our results with the current logistics network situation, and calculate the improvement of logistics cost to help the management to make decisions. Finally, in order to check the effectiveness of the proposed method, the results acquired are compared with those come from the two methods/ software, such as LINDO and CPLEX.

A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

NASA Astrophysics Data System (ADS)

Hsiao, Feng-Hsiag

2016-10-01

In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.

Engineering strategies for the fermentative production of plant alkaloids in yeast

PubMed Central

Trenchard, Isis J.; Smolke, Christina D.

2015-01-01

Microbial hosts engineered for the biosynthesis of plant natural products offer enormous potential as powerful discovery and production platforms. However, the reconstruction of these complex biosynthetic schemes faces numerous challenges due to the number of enzymatic steps and challenging enzyme classes associated with these pathways, which can lead to issues in metabolic load, pathway specificity, and maintaining flux to desired products. Cytochrome P450 enzymes are prevalent in plant specialized metabolism and are particularly difficult to express heterologously. Here, we describe the reconstruction of the sanguinarine branch of the benzylisoquinoline alkaloid pathway in Saccharomyces cerevisiae, resulting in microbial biosynthesis of protoberberine, protopine, and benzophenanthridine alkaloids through to the end-product sanguinarine, which we demonstrate can be efficiently produced in yeast in the absence of the associated biosynthetic enzyme. We achieved titers of 676 µg/L stylopine, 548 µg/L cis-N-methylstylopine, 252 µg/L protopine, and 80 µg/L sanguinarine from the engineered yeast strains. Through our optimization efforts, we describe genetic and culture strategies supporting the functional expression of multiple plant cytochrome P450 enzymes in the context of a large multi-step pathway. Our results also provided insight into relationships between cytochrome P450 activity and yeast ER physiology. We were able to improve the production of critical intermediates by 32-fold through genetic techniques and an additional 45-fold through culture optimization. PMID:25981946

Clients' interpretation of risks provided in genetic counseling.

PubMed Central

Wertz, D C; Sorenson, J R; Heeren, T C

1986-01-01

Clients in 544 genetic counseling sessions who were given numeric risks of having a child with a birth defect between 0% and 50% were asked to interpret these numeric risks on a five-point scale, ranging from very low to very high. Whereas clients' modal interpretation varied directly with numeric risks between 0% and 15%, the modal category of client risk interpretation remained "moderate" at risks between 15% and 50%. Uncertainty about normalcy of the next child increased as numeric risk increased, and few clients were willing to indicate that the child would probably or definitely be affected regardless of the numeric risk. Characteristics associated with clients' "pessimistic" interpretations of risk, identified by stepwise linear regression, included increased numeric risk, discussion in depth during the counseling session of whether they would have a child, have a living affected child, discussion of the effects of an affected child on relationships with client's other children, and seriousness of the disorder in question (causes intellectual impairment). Client interpretations are discussed in terms of recent developments in cognitive theory, including heuristics that influence judgments about risks, and implications for genetic counseling. PMID:3752089

Simulations of Flame Acceleration and DDT in Mixture Composition Gradients

NASA Astrophysics Data System (ADS)

Zheng, Weilin; Kaplan, Carolyn; Houim, Ryan; Oran, Elaine

2017-11-01

Unsteady, multidimensional, fully compressible numerical simulations of methane-air in an obstructed channel with spatial gradients in equivalence ratios have been carried to determine the effects of the gradients on flame acceleration and transition to detonation. Results for gradients perpendicular to the propagation direction were considered here. A calibrated, optimized chemical-diffusive model that reproduces correct flame and detonation properties for methane-air over a range of equivalence ratios was derived from a combination of a genetic algorithm with a Nelder-Mead optimization scheme. Inhomogeneous mixtures of methane-air resulted in slower flame acceleration and longer distance to DDT. Detonations were more likely to decouple into a flame and a shock under sharper concentration gradients. Detailed analyses of temperature and equivalence ratio illustrated that vertical gradients can greatly affect the formation of hot spots that initiate detonation by changing the strength of leading shock wave and local equivalence ratio near the base of obstacles. This work is supported by the Alpha Foundation (Grant No. AFC215-20).

A comparative study between different approaches to improve the RCS of a compact double-layer absorber

NASA Astrophysics Data System (ADS)

El-Hakim, H. A.; Mahmoud, K. R.

2017-10-01

In this paper, straightforward and efficient techniques have been addressed into double-layer structure to enlarge the operating bandwidth to include the X, Ku and K bands, in addition to increase the electromagnetic wave absorption for wide varieties of incident angles and both polarization types. To increase the band-stop resonating frequency up to 26 GHz, an additional layer of meta-surface, circuit analog radar absorber material (CAR), or a thin radar absorber material (RAM) layer is engineered. The synthesized layers are designed based on optimization process with genetic algorithm (GA) through numerical technique (Ansoft design software HFSS) for both transmission line (T.L) and the free space method to get optimal material properties suitable for the design. For different approaches, the designed structures achieved a reflectivity value less than -16 dB on average in the desired bandwidth from 8 to 26 GHz for TE/TM modes with incidence angle up to 50o.

Optimized thin film coatings for passive radiative cooling applications

NASA Astrophysics Data System (ADS)

Naghshine, Babak B.; Saboonchi, Ahmad

2018-03-01

Passive radiative cooling is a very interesting method, which lays on low atmospheric downward radiation within 8-13 μm waveband at dry climates. Various thin film multilayer structures have been investigated in numerous experimental studies, in order to find better coatings to exploit the full potential of this method. However, theoretical works are handful and limited. In this paper, the Simulated Annealing and Genetic Algorithm are used to optimize a thin film multilayer structure for passive radiative cooling applications. Spectral radiative properties are calculated through the matrix formulation. Considering a wide range of materials, 30 high-potential convective shields are suggested. According to the calculations, cooling can be possible even under direct sunlight, using the introduced shields. Moreover, a few water-soluble materials are studied for the first time and the results show that, a KBr substrate coated by a thin CaF2 or polyethylene film can is very close to an ideal coating for passive radiative cooling at night.

An Optimization Model for the Selection of Bus-Only Lanes in a City.

PubMed

Chen, Qun

2015-01-01

The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers' route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model.

OPC modeling by genetic algorithm

NASA Astrophysics Data System (ADS)

Huang, W. C.; Lai, C. M.; Luo, B.; Tsai, C. K.; Tsay, C. S.; Lai, C. W.; Kuo, C. C.; Liu, R. G.; Lin, H. T.; Lin, B. J.

2005-05-01

Optical proximity correction (OPC) is usually used to pre-distort mask layouts to make the printed patterns as close to the desired shapes as possible. For model-based OPC, a lithographic model to predict critical dimensions after lithographic processing is needed. The model is usually obtained via a regression of parameters based on experimental data containing optical proximity effects. When the parameters involve a mix of the continuous (optical and resist models) and the discrete (kernel numbers) sets, the traditional numerical optimization method may have difficulty handling model fitting. In this study, an artificial-intelligent optimization method was used to regress the parameters of the lithographic models for OPC. The implemented phenomenological models were constant-threshold models that combine diffused aerial image models with loading effects. Optical kernels decomposed from Hopkin"s equation were used to calculate aerial images on the wafer. Similarly, the numbers of optical kernels were treated as regression parameters. This way, good regression results were obtained with different sets of optical proximity effect data.

Numerical modeling and optimization of the Iguassu gas centrifuge

NASA Astrophysics Data System (ADS)

Bogovalov, S. V.; Borman, V. D.; Borisevich, V. D.; Tronin, V. N.; Tronin, I. V.

2017-07-01

The full procedure of the numerical calculation of the optimized parameters of the Iguassu gas centrifuge (GC) is under discussion. The procedure consists of a few steps. On the first step the problem of a hydrodynamical flow of the gas in the rotating rotor of the GC is solved numerically. On the second step the problem of diffusion of the binary mixture of isotopes is solved. The separation power of the gas centrifuge is calculated after that. On the last step the time consuming procedure of optimization of the GC is performed providing us the maximum of the separation power. The optimization is based on the BOBYQA method exploring the results of numerical simulations of the hydrodynamics and diffusion of the mixture of isotopes. Fast convergence of calculations is achieved due to exploring of a direct solver at the solution of the hydrodynamical and diffusion parts of the problem. Optimized separative power and optimal internal parameters of the Iguassu GC with 1 m rotor were calculated using the developed approach. Optimization procedure converges in 45 iterations taking 811 minutes.

Optimization methods and silicon solar cell numerical models

NASA Technical Reports Server (NTRS)

Girardini, K.; Jacobsen, S. E.

1986-01-01

An optimization algorithm for use with numerical silicon solar cell models was developed. By coupling an optimization algorithm with a solar cell model, it is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junction depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm was developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAP1D). SCAP1D uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the performance of a solar cell. A major obstacle is that the numerical methods used in SCAP1D require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the values associated with the maximum efficiency. This problem was alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution.

Numerical optimization methods for controlled systems with parameters

NASA Astrophysics Data System (ADS)

Tyatyushkin, A. I.

2017-10-01

First- and second-order numerical methods for optimizing controlled dynamical systems with parameters are discussed. In unconstrained-parameter problems, the control parameters are optimized by applying the conjugate gradient method. A more accurate numerical solution in these problems is produced by Newton's method based on a second-order functional increment formula. Next, a general optimal control problem with state constraints and parameters involved on the righthand sides of the controlled system and in the initial conditions is considered. This complicated problem is reduced to a mathematical programming one, followed by the search for optimal parameter values and control functions by applying a multimethod algorithm. The performance of the proposed technique is demonstrated by solving application problems.

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

Numerical Optimization Using Computer Experiments

NASA Technical Reports Server (NTRS)

Trosset, Michael W.; Torczon, Virginia

1997-01-01

Engineering design optimization often gives rise to problems in which expensive objective functions are minimized by derivative-free methods. We propose a method for solving such problems that synthesizes ideas from the numerical optimization and computer experiment literatures. Our approach relies on kriging known function values to construct a sequence of surrogate models of the objective function that are used to guide a grid search for a minimizer. Results from numerical experiments on a standard test problem are presented.

Design of a correlated validated CFD and genetic algorithm model for optimized sensors placement for indoor air quality monitoring

NASA Astrophysics Data System (ADS)

Mousavi, Monireh Sadat; Ashrafi, Khosro; Motlagh, Majid Shafie Pour; Niksokhan, Mohhamad Hosein; Vosoughifar, HamidReza

2018-02-01

In this study, coupled method for simulation of flow pattern based on computational methods for fluid dynamics with optimization technique using genetic algorithms is presented to determine the optimal location and number of sensors in an enclosed residential complex parking in Tehran. The main objective of this research is costs reduction and maximum coverage with regard to distribution of existing concentrations in different scenarios. In this study, considering all the different scenarios for simulation of pollution distribution using CFD simulations has been challenging due to extent of parking and number of cars available. To solve this problem, some scenarios have been selected based on random method. Then, maximum concentrations of scenarios are chosen for performing optimization. CFD simulation outputs are inserted as input in the optimization model using genetic algorithm. The obtained results stated optimal number and location of sensors.

The optimal design of UAV wing structure

NASA Astrophysics Data System (ADS)

Długosz, Adam; Klimek, Wiktor

2018-01-01

The paper presents an optimal design of UAV wing, made of composite materials. The aim of the optimization is to improve strength and stiffness together with reduction of the weight of the structure. Three different types of functionals, which depend on stress, stiffness and the total mass are defined. The paper presents an application of the in-house implementation of the evolutionary multi-objective algorithm in optimization of the UAV wing structure. Values of the functionals are calculated on the basis of results obtained from numerical simulations. Numerical FEM model, consisting of different composite materials is created. Adequacy of the numerical model is verified by results obtained from the experiment, performed on a tensile testing machine. Examples of multi-objective optimization by means of Pareto-optimal set of solutions are presented.

Nuclear fuel management optimization using genetic algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-07-01

The code independent genetic algorithm reactor optimization (CIGARO) system has been developed to optimize nuclear reactor loading patterns. It uses genetic algorithms (GAs) and a code-independent interface, so any reactor physics code (e.g., CASMO-3/SIMULATE-3) can be used to evaluate the loading patterns. The system is compared to other GA-based loading pattern optimizers. Tests were carried out to maximize the beginning of cycle k{sub eff} for a pressurized water reactor core loading with a penalty function to limit power peaking. The CIGARO system performed well, increasing the k{sub eff} after lowering the peak power. Tests of a prototype parallel evaluation methodmore » showed the potential for a significant speedup.« less

The use of a genetic algorithm-based search strategy in geostatistics: application to a set of anisotropic piezometric head data

NASA Astrophysics Data System (ADS)

Abedini, M. J.; Nasseri, M.; Burn, D. H.

2012-04-01

In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.

Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment.

PubMed

Wang, Lin; Qu, Hui; Liu, Shan; Dun, Cai-xia

2013-01-01

As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted.

Modeling and Optimization of the Multiobjective Stochastic Joint Replenishment and Delivery Problem under Supply Chain Environment

PubMed Central

Dun, Cai-xia

2013-01-01

As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted. PMID:24302880

Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft

NASA Technical Reports Server (NTRS)

Wells, Valana L.

1996-01-01

This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.

Optimisation multi-objectif des systemes energetiques

NASA Astrophysics Data System (ADS)

Dipama, Jean

The increasing demand of energy and the environmental concerns related to greenhouse gas emissions lead to more and more private or public utilities to turn to nuclear energy as an alternative for the future. Nuclear power plants are then called to experience large expansion in the coming years. Improved technologies will then be put in place to support the development of these plants. This thesis considers the optimization of the thermodynamic cycle of the secondary loop of Gentilly-2 nuclear power plant in terms of output power and thermal efficiency. In this thesis, investigations are carried out to determine the optimal operating conditions of steam power cycles by the judicious use of the combination of steam extraction at the different stages of the turbines. Whether it is the case of superheating or regeneration, we are confronted in all cases to an optimization problem involving two conflicting objectives, as increasing the efficiency imply the decrease of mechanical work and vice versa. Solving this kind of problem does not lead to unique solution, but to a set of solutions that are tradeoffs between the conflicting objectives. To search all of these solutions, called Pareto optimal solutions, the use of an appropriate optimization algorithm is required. Before starting the optimization of the secondary loop, we developed a thermodynamic model of the secondary loop which includes models for the main thermal components (e.g., turbine, moisture separator-superheater, condenser, feedwater heater and deaerator). This model is used to calculate the thermodynamic state of the steam and water at the different points of the installation. The thermodynamic model has been developed with Matlab and validated by comparing its predictions with the operating data provided by the engineers of the power plant. The optimizer developed in VBA (Visual Basic for Applications) uses an optimization algorithm based on the principle of genetic algorithms, a stochastic optimization method which is very robust and widely used to solve problems usually difficult to handle by traditional methods. Genetic algorithms (GAs) have been used in previous research and proved to be efficient in optimizing heat exchangers networks (HEN) (Dipama et al., 2008). So, HEN have been synthesized to recover the maximum heat in an industrial process. The optimization problem formulated in the context of this work consists of a single objective, namely the maximization of energy recovery. The optimization algorithm developed in this thesis extends the ability of GAs by taking into account several objectives simultaneously. This algorithm provides an innovation in the method of finding optimal solutions, by using a technique which consist of partitioning the solutions space in the form of parallel grids called "watching corridors". These corridors permit to specify areas (the observation corridors) in which the most promising feasible solutions are found and used to guide the search towards optimal solutions. A measure of the progress of the search is incorporated into the optimization algorithm to make it self-adaptive through the use of appropriate genetic operators at each stage of optimization process. The proposed method allows a fast convergence and ensure a diversity of solutions. Moreover, this method gives the algorithm the ability to overcome difficulties associated with optimizing problems with complex Pareto front landscapes (e.g., discontinuity, disjunction, etc.). The multi-objective optimization algorithm has been first validated using numerical test problems found in the literature as well as energy systems optimization problems. Finally, the proposed optimization algorithm has been applied for the optimization of the secondary loop of Gentilly-2 nuclear power plant, and a set of solutions have been found which permit to make the power plant operate in optimal conditions. (Abstract shortened by UMI.)

Influence of cost functions and optimization methods on solving the inverse problem in spatially resolved diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Rakotomanga, Prisca; Soussen, Charles; Blondel, Walter C. P. M.

2017-03-01

Diffuse reflectance spectroscopy (DRS) has been acknowledged as a valuable optical biopsy tool for in vivo characterizing pathological modifications in epithelial tissues such as cancer. In spatially resolved DRS, accurate and robust estimation of the optical parameters (OP) of biological tissues is a major challenge due to the complexity of the physical models. Solving this inverse problem requires to consider 3 components: the forward model, the cost function, and the optimization algorithm. This paper presents a comparative numerical study of the performances in estimating OP depending on the choice made for each of the latter components. Mono- and bi-layer tissue models are considered. Monowavelength (scalar) absorption and scattering coefficients are estimated. As a forward model, diffusion approximation analytical solutions with and without noise are implemented. Several cost functions are evaluated possibly including normalized data terms. Two local optimization methods, Levenberg-Marquardt and TrustRegion-Reflective, are considered. Because they may be sensitive to the initial setting, a global optimization approach is proposed to improve the estimation accuracy. This algorithm is based on repeated calls to the above-mentioned local methods, with initial parameters randomly sampled. Two global optimization methods, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are also implemented. Estimation performances are evaluated in terms of relative errors between the ground truth and the estimated values for each set of unknown OP. The combination between the number of variables to be estimated, the nature of the forward model, the cost function to be minimized and the optimization method are discussed.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Clues to the Foundations of Numerical Cognitive Impairments: Evidence From Genetic Disorders

PubMed Central

Simon, Tony J.

2011-01-01

Several neurodevelopmental disorders of known genetic etiology generate phenotypes that share the characteristic of numerical and mathematical cognitive impairments. This article reviews some of the main findings that suggest a possible key role that spatial and temporal information processing impairments may play in the atypical development of numerical cognitive competence. The question of what neural substrate might underlie these impairments is also addressed, as are the challenges for interpreting neural structure/cognitive function mapping in atypically developing populations. PMID:21761998

A sensitivity equation approach to shape optimization in fluid flows

NASA Technical Reports Server (NTRS)

Borggaard, Jeff; Burns, John

1994-01-01

A sensitivity equation method to shape optimization problems is applied. An algorithm is developed and tested on a problem of designing optimal forebody simulators for a 2D, inviscid supersonic flow. The algorithm uses a BFGS/Trust Region optimization scheme with sensitivities computed by numerically approximating the linear partial differential equations that determine the flow sensitivities. Numerical examples are presented to illustrate the method.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

Genetically Engineered Cyanobacteria

NASA Technical Reports Server (NTRS)

Zhou, Ruanbao (Inventor); Gibbons, William (Inventor)

2015-01-01

The disclosed embodiments provide cyanobacteria spp. that have been genetically engineered to have increased production of carbon-based products of interest. These genetically engineered hosts efficiently convert carbon dioxide and light into carbon-based products of interest such as long chained hydrocarbons. Several constructs containing polynucleotides encoding enzymes active in the metabolic pathways of cyanobacteria are disclosed. In many instances, the cyanobacteria strains have been further genetically modified to optimize production of the carbon-based products of interest. The optimization includes both up-regulation and down-regulation of particular genes.

Validation of morphing wing methodologies on an unmanned aerial system and a wind tunnel technology demonstrator

NASA Astrophysics Data System (ADS)

Gabor, Oliviu Sugar

To increase the aerodynamic efficiency of aircraft, in order to reduce the fuel consumption, a novel morphing wing concept has been developed. It consists in replacing a part of the wing upper and lower surfaces with a flexible skin whose shape can be modified using an actuation system placed inside the wing structure. Numerical studies in two and three dimensions were performed in order to determine the gains the morphing system achieves for the case of an Unmanned Aerial System and for a morphing technology demonstrator based on the wing tip of a transport aircraft. To obtain the optimal wing skin shapes in function of the flight condition, different global optimization algorithms were implemented, such as the Genetic Algorithm and the Artificial Bee Colony Algorithm. To reduce calculation times, a hybrid method was created by coupling the population-based algorithm with a fast, gradient-based local search method. Validations were performed with commercial state-of-the-art optimization tools and demonstrated the efficiency of the proposed methods. For accurately determining the aerodynamic characteristics of the morphing wing, two new methods were developed, a nonlinear lifting line method and a nonlinear vortex lattice method. Both use strip analysis of the span-wise wing section to account for the airfoil shape modifications induced by the flexible skin, and can provide accurate results for the wing drag coefficient. The methods do not require the generation of a complex mesh around the wing and are suitable for coupling with optimization algorithms due to the computational time several orders of magnitude smaller than traditional three-dimensional Computational Fluid Dynamics methods. Two-dimensional and three-dimensional optimizations of the Unmanned Aerial System wing equipped with the morphing skin were performed, with the objective of improving its performances for an extended range of flight conditions. The chordwise positions of the internal actuators, the spanwise number of actuation stations as well as the displacement limits were established. The performance improvements obtained and the limitations of the morphing wing concept were studied. To verify the optimization results, high-fidelity Computational Fluid Dynamics simulations were also performed, giving very accurate indications of the obtained gains. For the morphing model based on an aircraft wing tip, the skin shapes were optimized in order to control laminar flow on the upper surface. An automated structured mesh generation procedure was developed and implemented. To accurately capture the shape of the skin, a precision scanning procedure was done and its results were included in the numerical model. High-fidelity simulations were performed to determine the upper surface transition region and the numerical results were validated using experimental wind tunnel data.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem

PubMed Central

Yu, Yi; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. PMID:29324743

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.

PubMed

Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

Economic trade-offs between genetic improvement and longevity in dairy cattle.

PubMed

De Vries, A

2017-05-01

Genetic improvement in sires used for artificial insemination (AI) is increasing faster compared with a decade ago. The genetic merit of replacement heifers is also increasing faster and the genetic lag with older cows in the herd increases. This may trigger greater cow culling to capture this genetic improvement. On the other hand, lower culling rates are often viewed favorably because the costs and environmental effects of maintaining herd size are generally lower. Thus, there is an economic trade-off between genetic improvement and longevity in dairy cattle. The objective of this study was to investigate the principles, literature, and magnitude of these trade-offs. Data from the Council on Dairy Cattle Breeding show that the estimated breeding value of the trait productive life has increased for 50 yr but the actual time cows spend in the herd has not increased. The average annual herd cull rate remains at approximately 36% and cow longevity is approximately 59 mo. The annual increase in average estimated breeding value of the economic index lifetime net merit of Holstein sires is accelerating from $40/yr when the sire entered AI around 2002 to $171/yr for sires that entered AI around 2012. The expectation is therefore that heifers born in 2015 are approximately $50 more profitable per lactation than heifers born in 2014. Asset replacement theory shows that assets should be replaced sooner when the challenging asset is technically improved. Few studies have investigated the direct effects of genetic improvement on optimal cull rates. A 35-yr-old study found that the economically optimal cull rates were in the range of 25 to 27%, compared with the lowest possible involuntary cull rate of 20%. Only a small effect was observed of using the best surviving dams to generate the replacement heifer calves. Genetic improvement from sires had little effect on the optimal cull rate. Another study that optimized culling decisions for individual cows also showed that the effect of changes in genetic improvement of milk revenue minus feed cost on herd longevity was relatively small. Reduced involuntary cull rates improved profitability, but also increased optimal voluntary culling. Finally, an economically optimal culling model with prices from 2015 confirmed that optimal annual cull rates were insensitive to heifer prices and therefore insensitive to genetic improvement in heifers. In conclusion, genetic improvement is important but does not warrant short cow longevity. Economic cow longevity continues to depends more on cow depreciation than on accelerated genetic improvements in heifers. This is confirmed by old and new studies. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Numerical Magnitude Processing Impairments in Genetic Syndromes: A Cross-Syndrome Comparison of Turner and 22Q11.2 Deletion Syndromes

ERIC Educational Resources Information Center

Brankaer, Carmen; Ghesquière, Pol; De Wel, Anke; Swillen, Ann; De Smedt, Bert

2017-01-01

Cross-syndrome comparisons offer an important window onto understanding heterogeneity in mathematical learning disabilities or dyscalculia. The present study therefore investigated symbolic numerical magnitude processing in two genetic syndromes that are both characterized by mathematical learning disabilities: Turner syndrome and 22q11.2 deletion…

Automatic genetic optimization approach to two-dimensional blade profile design for steam turbines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trigg, M.A.; Tubby, G.R.; Sheard, A.G.

1999-01-01

In this paper a systematic approach to the optimization of two-dimensional blade profiles is presented. A genetic optimizer has been developed that modifies the blade profile and calculates its profile loss. This process is automatic, producing profile designs significantly faster and with significantly lower loss than has previously been possible. The optimizer developed uses a genetic algorithm to optimize a two-dimensional profile, defined using 17 parameters, for minimum loss with a given flow condition. The optimizer works with a population of two-dimensional profiles with varied parameters. A CFD mesh is generated for each profile, and the result is analyzed usingmore » a two-dimensional blade-to-blade solver, written for steady viscous compressible flow, to determine profile loss. The loss is used as the measure of a profile`s fitness. The optimizer uses this information to select the members of the next population, applying crossovers, mutations, and elitism in the process. Using this method, the optimizer tends toward the best values for the parameters defining the profile with minimum loss.« less

Genetic algorithm to optimize the design of main combustor and gas generator in liquid rocket engines

NASA Astrophysics Data System (ADS)

Son, Min; Ko, Sangho; Koo, Jaye

2014-06-01

A genetic algorithm was used to develop optimal design methods for the regenerative cooled combustor and fuel-rich gas generator of a liquid rocket engine. For the combustor design, a chemical equilibrium analysis was applied, and the profile was calculated using Rao's method. One-dimensional heat transfer was assumed along the profile, and cooling channels were designed. For the gas-generator design, non-equilibrium properties were derived from a counterflow analysis, and a vaporization model for the fuel droplet was adopted to calculate residence time. Finally, a genetic algorithm was adopted to optimize the designs. The combustor and gas generator were optimally designed for 30-tonf, 75-tonf, and 150-tonf engines. The optimized combustors demonstrated superior design characteristics when compared with previous non-optimized results. Wall temperatures at the nozzle throat were optimized to satisfy the requirement of 800 K, and specific impulses were maximized. In addition, the target turbine power and a burned-gas temperature of 1000 K were obtained from the optimized gas-generator design.

Springback optimization in automotive Shock Absorber Cup with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kakandikar, Ganesh; Nandedkar, Vilas

2018-02-01

Drawing or forming is a process normally used to achieve a required component form from a metal blank by applying a punch which radially draws the blank into the die by a mechanical or hydraulic action or combining both. When the component is drawn for more depth than the diameter, it is usually seen as deep drawing, which involves complicated states of material deformation. Due to the radial drawing of the material as it enters the die, radial drawing stress occurs in the flange with existence of the tangential compressive stress. This compression generates wrinkles in the flange. Wrinkling is unwanted phenomenon and can be controlled by application of a blank-holding force. Tensile stresses cause thinning in the wall region of the cup. Three main types of the errors occur in such a process are wrinkling, fracturing and springback. This paper reports a work focused on the springback and control. Due to complexity of the process, tool try-outs and experimentation may be costly, bulky and time consuming. Numerical simulation proves to be a good option for studying the process and developing a control strategy for reducing the springback. Finite-element based simulations have been used popularly for such purposes. In this study, the springback in deep drawing of an automotive Shock Absorber Cup is simulated with finite element method. Taguchi design of experiments and analysis of variance are used to analyze the influencing process parameters on the springback. Mathematical relations are developed to relate the process parameters and the resulting springback. The optimization problem is formulated for the springback, referring to the displacement magnitude in the selected sections. Genetic Algorithm is then applied for process optimization with an objective to minimize the springback. The results indicate that a better prediction of the springback and process optimization could be achieved with a combined use of these methods and tools.

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

DOE PAGES

Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; ...

2014-10-15

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

Genetic code, hamming distance and stochastic matrices.

PubMed

He, Matthew X; Petoukhov, Sergei V; Ricci, Paolo E

2004-09-01

In this paper we use the Gray code representation of the genetic code C=00, U=10, G=11 and A=01 (C pairs with G, A pairs with U) to generate a sequence of genetic code-based matrices. In connection with these code-based matrices, we use the Hamming distance to generate a sequence of numerical matrices. We then further investigate the properties of the numerical matrices and show that they are doubly stochastic and symmetric. We determine the frequency distributions of the Hamming distances, building blocks of the matrices, decomposition and iterations of matrices. We present an explicit decomposition formula for the genetic code-based matrix in terms of permutation matrices, which provides a hypercube representation of the genetic code. It is also observed that there is a Hamiltonian cycle in a genetic code-based hypercube.

Computing the optimal path in stochastic dynamical systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bauver, Martha; Forgoston, Eric, E-mail: eric.forgoston@montclair.edu; Billings, Lora

2016-08-15

In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensionalmore » system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.« less

Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.

Motivations for genetic testing for lung cancer risk among young smokers.

PubMed

O'Neill, Suzanne C; Lipkus, Isaac M; Sanderson, Saskia C; Shepperd, James; Docherty, Sharron; McBride, Colleen M

2013-11-01

To examine why young people might want to undergo genetic susceptibility testing for lung cancer despite knowing that tested gene variants are associated with small increases in disease risk. The authors used a mixed-method approach to evaluate motives for and against genetic testing and the association between these motivations and testing intentions in 128 college students who smoke. Exploratory factor analysis yielded four reliable factors: Test Scepticism, Test Optimism, Knowledge Enhancement and Smoking Optimism. Test Optimism and Knowledge Enhancement correlated positively with intentions to test in bivariate and multivariate analyses (ps<0.001). Test Scepticism correlated negatively with testing intentions in multivariate analyses (p<0.05). Open-ended questions assessing testing motivations generally replicated themes of the quantitative survey. In addition to learning about health risks, young people may be motivated to seek genetic testing for reasons, such as gaining knowledge about new genetic technologies more broadly.

A multi-resolution approach for optimal mass transport

NASA Astrophysics Data System (ADS)

Dominitz, Ayelet; Angenent, Sigurd; Tannenbaum, Allen

2007-09-01

Optimal mass transport is an important technique with numerous applications in econometrics, fluid dynamics, automatic control, statistical physics, shape optimization, expert systems, and meteorology. Motivated by certain problems in image registration and medical image visualization, in this note, we describe a simple gradient descent methodology for computing the optimal L2 transport mapping which may be easily implemented using a multiresolution scheme. We also indicate how the optimal transport map may be computed on the sphere. A numerical example is presented illustrating our ideas.

Optimality conditions for the numerical solution of optimization problems with PDE constraints :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aguilo Valentin, Miguel Alejandro; Ridzal, Denis

2014-03-01

A theoretical framework for the numerical solution of partial di erential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to e ciently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identi cation and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

Application of Differential Evolutionary Optimization Methodology for Parameter Structure Identification in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.

2013-12-01

With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.

Particle swarm optimization with recombination and dynamic linkage discovery.

PubMed

Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

2007-12-01

In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

Multiple burn fuel-optimal orbit transfers: Numerical trajectory computation and neighboring optimal feedback guidance

NASA Technical Reports Server (NTRS)

Chuang, C.-H.; Goodson, Troy D.; Ledsinger, Laura A.

1995-01-01

This report describes current work in the numerical computation of multiple burn, fuel-optimal orbit transfers and presents an analysis of the second variation for extremal multiple burn orbital transfers as well as a discussion of a guidance scheme which may be implemented for such transfers. The discussion of numerical computation focuses on the use of multivariate interpolation to aid the computation in the numerical optimization. The second variation analysis includes the development of the conditions for the examination of both fixed and free final time transfers. Evaluations for fixed final time are presented for extremal one, two, and three burn solutions of the first variation. The free final time problem is considered for an extremal two burn solution. In addition, corresponding changes of the second variation formulation over thrust arcs and coast arcs are included. The guidance scheme discussed is an implicit scheme which implements a neighboring optimal feedback guidance strategy to calculate both thrust direction and thrust on-off times.

Genetic-evolution-based optimization methods for engineering design

NASA Technical Reports Server (NTRS)

Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.

1990-01-01

This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

Genetic Algorithm Optimization of Phononic Bandgap Structures

DTIC Science & Technology

2006-09-01

a GA with a computational finite element method for solving the acoustic wave equation, and find optimal designs for both metal-matrix composite...systems consisting of Ti/SiC, and H2O-filled porous ceramic media, by maximizing the relative acoustic bandgap for these media. The term acoustic here...stress minimization, global optimization, phonon bandgap, genetic algorithm, periodic elastic media, inhomogeneity, inclusion, porous media, acoustic

Enhancing (crop) plant photosynthesis by introducing novel genetic diversity.

PubMed

Dann, Marcel; Leister, Dario

2017-09-26

Although some elements of the photosynthetic light reactions might appear to be ideal, the overall efficiency of light conversion to biomass has not been optimized during evolution. Because crop plants are depleted of genetic diversity for photosynthesis, efforts to enhance its efficiency with respect to light conversion to yield must generate new variation. In principle, three sources of natural variation are available: (i) rare diversity within extant higher plant species, (ii) photosynthetic variants from algae, and (iii) reconstruction of no longer extant types of plant photosynthesis. Here, we argue for a novel approach that outsources crop photosynthesis to a cyanobacterium that is amenable to adaptive evolution. This system offers numerous advantages, including a short generation time, virtually unlimited population sizes and high mutation rates, together with a versatile toolbox for genetic manipulation. On such a synthetic bacterial platform, 10 000 years of (crop) plant evolution can be recapitulated within weeks. Limitations of this system arise from its unicellular nature, which cannot reproduce all aspects of crop photosynthesis. But successful establishment of such a bacterial host for crop photosynthesis promises not only to enhance the performance of eukaryotic photosynthesis but will also reveal novel facets of the molecular basis of photosynthetic flexibility.This article is part of the themed issue 'Enhancing photosynthesis in crop plants: targets for improvement'. © 2017 The Author(s).

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

PubMed

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

PubMed Central

Salehi, Mojtaba

2010-01-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

The high-quality genome of Brassica napus cultivar 'ZS11' reveals the introgression history in semi-winter morphotype.

PubMed

Sun, Fengming; Fan, Guangyi; Hu, Qiong; Zhou, Yongming; Guan, Mei; Tong, Chaobo; Li, Jiana; Du, Dezhi; Qi, Cunkou; Jiang, Liangcai; Liu, Weiqing; Huang, Shunmou; Chen, Wenbin; Yu, Jingyin; Mei, Desheng; Meng, Jinling; Zeng, Peng; Shi, Jiaqin; Liu, Kede; Wang, Xi; Wang, Xinfa; Long, Yan; Liang, Xinming; Hu, Zhiyong; Huang, Guodong; Dong, Caihua; Zhang, He; Li, Jun; Zhang, Yaolei; Li, Liangwei; Shi, Chengcheng; Wang, Jiahao; Lee, Simon Ming-Yuen; Guan, Chunyun; Xu, Xun; Liu, Shengyi; Liu, Xin; Chalhoub, Boulos; Hua, Wei; Wang, Hanzhong

2017-11-01

Allotetraploid oilseed rape (Brassica napus L.) is an agriculturally important crop. Cultivation and breeding of B. napus by humans has resulted in numerous genetically diverse morphotypes with optimized agronomic traits and ecophysiological adaptation. To further understand the genetic basis of diversification and adaptation, we report a draft genome of an Asian semi-winter oilseed rape cultivar 'ZS11' and its comprehensive genomic comparison with the genomes of the winter-type cultivar 'Darmor-bzh' as well as two progenitors. The integrated BAC-to-BAC and whole-genome shotgun sequencing strategies were effective in the assembly of repetitive regions (especially young long terminal repeats) and resulted in a high-quality genome assembly of B. napus 'ZS11'. Within a short evolutionary period (~6700 years ago), semi-winter-type 'ZS11' and the winter-type 'Darmor-bzh' maintained highly genomic collinearity. Even so, certain genetic differences were also detected in two morphotypes. Relative to 'Darmor-bzh', both two subgenomes of 'ZS11' are closely related to its progenitors, and the 'ZS11' genome harbored several specific segmental homoeologous exchanges (HEs). Furthermore, the semi-winter-type 'ZS11' underwent potential genomic introgressions with B. rapa (A r ). Some of these genetic differences were associated with key agronomic traits. A key gene of A03.FLC3 regulating vernalization-responsive flowering time in 'ZS11' was first experienced HE, and then underwent genomic introgression event with A r , which potentially has led to genetic differences in controlling vernalization in the semi-winter types. Our observations improved our understanding of the genetic diversity of different B. napus morphotypes and the cultivation history of semi-winter oilseed rape in Asia. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

A Genomic Resource for the Development, Improvement, and Exploitation of Sorghum for Bioenergy.

PubMed

Brenton, Zachary W; Cooper, Elizabeth A; Myers, Mathew T; Boyles, Richard E; Shakoor, Nadia; Zielinski, Kelsey J; Rauh, Bradley L; Bridges, William C; Morris, Geoffrey P; Kresovich, Stephen

2016-09-01

With high productivity and stress tolerance, numerous grass genera of the Andropogoneae have emerged as candidates for bioenergy production. To optimize these candidates, research examining the genetic architecture of yield, carbon partitioning, and composition is required to advance breeding objectives. Significant progress has been made developing genetic and genomic resources for Andropogoneae, and advances in comparative and computational genomics have enabled research examining the genetic basis of photosynthesis, carbon partitioning, composition, and sink strength. To provide a pivotal resource aimed at developing a comparative understanding of key bioenergy traits in the Andropogoneae, we have established and characterized an association panel of 390 racially, geographically, and phenotypically diverse Sorghum bicolor accessions with 232,303 genetic markers. Sorghum bicolor was selected because of its genomic simplicity, phenotypic diversity, significant genomic tools, and its agricultural productivity and resilience. We have demonstrated the value of sorghum as a functional model for candidate gene discovery for bioenergy Andropogoneae by performing genome-wide association analysis for two contrasting phenotypes representing key components of structural and non-structural carbohydrates. We identified potential genes, including a cellulase enzyme and a vacuolar transporter, associated with increased non-structural carbohydrates that could lead to bioenergy sorghum improvement. Although our analysis identified genes with potentially clear functions, other candidates did not have assigned functions, suggesting novel molecular mechanisms for carbon partitioning traits. These results, combined with our characterization of phenotypic and genetic diversity and the public accessibility of each accession and genomic data, demonstrate the value of this resource and provide a foundation for future improvement of sorghum and related grasses for bioenergy production. Copyright © 2016 by the Genetics Society of America.

Evolutionary speed of species invasions.

PubMed

García-Ramos, Gisela; Rodríguez, Diego

2002-04-01

Successful invasion may depend of the capacity of a species to adjust genetically to a spatially varying environment. This research modeled a species invasion by examining the interaction between a quantitative genetic trait and population density. It assumed: (I) a quantitative genetic trait describes the adaptation of an individual to its local ecological conditions; (2) populations far from the local optimum grow more slowly than those near the optimum; and (3) the evolution of a trait depends on local population density, because differences in local population densities cause asymmetrical gene flow. This genetics-density interaction determined the propagation speed of populations. Numerical simulations showed that populations spread by advancing as two synchronic traveling waves, one for population density and one for trait adaptation. The form of the density wave was a step front that advances homogenizing populations at their carrying capacity; the adaptation wave was a curve with finite slope that homogenizes populations at full adaptation. The largest speed of population expansion, for a dimensionless analysis, corresponded to an almost homogeneous spatial environment when this model approached an ecological description such as the Fisher-Skellam's model. A large genetic response also favored faster speeds. Evolutionary speeds, in a natural scale, showed a wide range of rates that were also slower compared to models that only consider demographics. This evolutionary speed increased with high heritability, strong stabilizing selection, and high intrinsic growth rate. It decreased for steeper environmental gradients. Also indicated was an optimal dispersal rate over which evolutionary speed declined. This is expected because dispersal moves individuals further, but homogenizes populations genetically, making them maladapted. The evolutionary speed was compared to observed data. Furthermore, a moderate increase in the speed of expansion was predicted for ecological changes related to global warming.

Genetic diagnosis from formalin-fixed fetal tissue using FISH: a new tool for genetic counseling in subsequent pregnancies.

PubMed

Fejgin, M D; Kidron, D; Kedar, I; Gaber, E; Tepper, R; Beyth, Y; Amiel, A

1996-02-01

We evaluated the feasibility of retrospective genetic testing for numerical chromosomal aberrations by applying the FISH technique to formalin-fixed fetal tissue. Fetal tissue from 10 old cases with known aneuploidy and from 13 cases with known fetal malformations, were tested with specific DNA probes for pericentromeric repeat regions of chromosomes 13/21, 18, X and Y. FISH diagnosis concurred with karyotype in all nine cases with sufficient cells. Numerical aberration was diagnosed in six out of 13 cases with fetal malformations.

A Genomic Resource for the Development, Improvement, and Exploitation of Sorghum for Bioenergy

PubMed Central

Brenton, Zachary W.; Cooper, Elizabeth A.; Myers, Mathew T.; Boyles, Richard E.; Shakoor, Nadia; Zielinski, Kelsey J.; Rauh, Bradley L.; Bridges, William C.; Morris, Geoffrey P.; Kresovich, Stephen

2016-01-01

With high productivity and stress tolerance, numerous grass genera of the Andropogoneae have emerged as candidates for bioenergy production. To optimize these candidates, research examining the genetic architecture of yield, carbon partitioning, and composition is required to advance breeding objectives. Significant progress has been made developing genetic and genomic resources for Andropogoneae, and advances in comparative and computational genomics have enabled research examining the genetic basis of photosynthesis, carbon partitioning, composition, and sink strength. To provide a pivotal resource aimed at developing a comparative understanding of key bioenergy traits in the Andropogoneae, we have established and characterized an association panel of 390 racially, geographically, and phenotypically diverse Sorghum bicolor accessions with 232,303 genetic markers. Sorghum bicolor was selected because of its genomic simplicity, phenotypic diversity, significant genomic tools, and its agricultural productivity and resilience. We have demonstrated the value of sorghum as a functional model for candidate gene discovery for bioenergy Andropogoneae by performing genome-wide association analysis for two contrasting phenotypes representing key components of structural and non-structural carbohydrates. We identified potential genes, including a cellulase enzyme and a vacuolar transporter, associated with increased non-structural carbohydrates that could lead to bioenergy sorghum improvement. Although our analysis identified genes with potentially clear functions, other candidates did not have assigned functions, suggesting novel molecular mechanisms for carbon partitioning traits. These results, combined with our characterization of phenotypic and genetic diversity and the public accessibility of each accession and genomic data, demonstrate the value of this resource and provide a foundation for future improvement of sorghum and related grasses for bioenergy production. PMID:27356613

In vivo gene delivery to the postnatal ferret cerebral cortex by DNA electroporation.

PubMed

Borrell, Víctor

2010-02-15

Ferrets have been extensively used to unravel the neural mechanisms of coding and processing of visual information, and also to identify the developmental mechanisms underlying the emergence of such a complex and fine-tuned neural system. In recent years numerous tools have been generated that allow studying neural systems with unprecedented power. Unfortunately, because many of these tools are genetically encoded, they are having a limited impact on research involving "non-genetic" species, like ferret, cat and monkey. Here I show how in vivo electroporation can be performed in postnatal ferret kits to deliver genetic constructs to pyramidal neurons of the cerebral cortex. Electroporation of GFP- and DsRed-encoding plasmids results in labeling of cortical progenitors first, then migrating neurons, and finally differentiating neurons and their processes. This technique also allows for the genetic manipulation of cortical development in the ferret, as illustrated by electroporation of a dominant-negative form of Cdk5. In the mature brain of electroporated animals, expression of reporter genes reveals the detailed morphological traits of cortical pyramids, including their axonal and dendritic arborization, and dendritic spines. I also show that postnatal electroporation can be used for the transfection of a massive cortical territory, or it can be specifically directed to a subset of cortical areas, and even only to a few scattered pyramids along the cortical mantle. In vivo electroporation of postnatal ferrets is therefore an effective, rapid, simple and highly versatile method for delivering genetic constructs to this animal, optimal for both developmental studies and adult anatomical/functional studies. Copyright 2009 Elsevier B.V. All rights reserved.

Fuel management optimization using genetic algorithms and code independence

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1994-12-31

Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs) address these problems and, therefore, appear ideal for fuel management optimization. They do not require derivative information and work well with combinatorial. functions. The GAs are a stochastic method based on concepts from biological genetics. They take a group of candidate solutions, called the population, and use selection, crossover, and mutation operators to create the next generation of bettermore » solutions. The selection operator is a {open_quotes}survival-of-the-fittest{close_quotes} operation and chooses the solutions for the next generation. The crossover operator is analogous to biological mating, where children inherit a mixture of traits from their parents, and the mutation operator makes small random changes to the solutions.« less

Optimization of Operations Resources via Discrete Event Simulation Modeling

NASA Technical Reports Server (NTRS)

Joshi, B.; Morris, D.; White, N.; Unal, R.

1996-01-01

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

2015-06-01

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

2017-02-01

Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

Personality traits associated with genetic counselor compassion fatigue: the roles of dispositional optimism and locus of control.

PubMed

Injeyan, Marie C; Shuman, Cheryl; Shugar, Andrea; Chitayat, David; Atenafu, Eshetu G; Kaiser, Amy

2011-10-01

Compassion fatigue (CMF) arises as a consequence of secondary exposure to distress and can be elevated in some health practitioners. Locus of control and dispositional optimism are aspects of personality known to influence coping style. To investigate whether these personality traits influence CMF risk, we surveyed 355 genetic counselors about their CMF, locus of control orientation, and degree of dispositional optimism. Approximately half of respondents reported they experience CMF; 26.6% had considered leaving their job due to CMF symptoms. Mixed-method analyses revealed that genetic counselors having an external locus of control and low optimism were at highest risk for CMF. Those at highest risk experienced moderate-to-high burnout, low-to-moderate compassion satisfaction, and tended to rely on religion/spirituality when coping with stress. CMF risk was not influenced by years in practice, number of genetic counselor colleagues in the workplace, or completion of graduate training in this area. Recommendations for practice and education are outlined.

A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem

NASA Astrophysics Data System (ADS)

Moradgholi, Mostafa; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid

2016-09-01

Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.

Estimation of the Reactive Flow Model Parameters for an Ammonium Nitrate-Based Emulsion Explosive Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Ribeiro, J. B.; Silva, C.; Mendes, R.

2010-10-01

A real coded genetic algorithm methodology that has been developed for the estimation of the parameters of the reaction rate equation of the Lee-Tarver reactive flow model is described in detail. This methodology allows, in a single optimization procedure, using only one experimental result and, without the need of any starting solution, to seek the 15 parameters of the reaction rate equation that fit the numerical to the experimental results. Mass averaging and the plate-gap model have been used for the determination of the shock data used in the unreacted explosive JWL equation of state (EOS) assessment and the thermochemical code THOR retrieved the data used in the detonation products' JWL EOS assessments. The developed methodology was applied for the estimation of the referred parameters for an ammonium nitrate-based emulsion explosive using poly(methyl methacrylate) (PMMA)-embedded manganin gauge pressure-time data. The obtained parameters allow a reasonably good description of the experimental data and show some peculiarities arising from the intrinsic nature of this kind of composite explosive.

Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

NASA Technical Reports Server (NTRS)

Lan, C. Edward; Ge, Fuying

1989-01-01

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

PubMed

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

PubMed Central

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055

Comparison of polynomial approximations and artificial neural nets for response surfaces in engineering optimization

NASA Technical Reports Server (NTRS)

Carpenter, William C.

1991-01-01

Engineering optimization problems involve minimizing some function subject to constraints. In areas such as aircraft optimization, the constraint equations may be from numerous disciplines such as transfer of information between these disciplines and the optimization algorithm. They are also suited to problems which may require numerous re-optimizations such as in multi-objective function optimization or to problems where the design space contains numerous local minima, thus requiring repeated optimizations from different initial designs. Their use has been limited, however, by the fact that development of response surfaces randomly selected or preselected points in the design space. Thus, they have been thought to be inefficient compared to algorithms to the optimum solution. A development has taken place in the last several years which may effect the desirability of using response surfaces. It may be possible that artificial neural nets are more efficient in developing response surfaces than polynomial approximations which have been used in the past. This development is the concern of the work.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Applications of hybrid genetic algorithms in seismic tomography

NASA Astrophysics Data System (ADS)

Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet T.; Papazachos, Constantinos

2011-11-01

Almost all earth sciences inverse problems are nonlinear and involve a large number of unknown parameters, making the application of analytical inversion methods quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem equations, adopting an iterative procedure which typically employs partial derivatives in order to optimize the starting (initial) model by minimizing a misfit (penalty) function. Unfortunately, especially for highly non-linear cases, the final model strongly depends on the initial model, hence it is prone to solution-entrapment in local minima of the misfit function, while the derivative calculation is often computationally inefficient and creates instabilities when numerical approximations are used. An alternative is to employ global techniques which do not rely on partial derivatives, are independent of the misfit form and are computationally robust. Such methods employ pseudo-randomly generated models (sampling an appropriately selected section of the model space) which are assessed in terms of their data-fit. A typical example is the class of methods known as genetic algorithms (GA), which achieves the aforementioned approximation through model representation and manipulations, and has attracted the attention of the earth sciences community during the last decade, with several applications already presented for several geophysical problems. In this paper, we examine the efficiency of the combination of the typical regularized least-squares and genetic methods for a typical seismic tomography problem. The proposed approach combines a local (LOM) and a global (GOM) optimization method, in an attempt to overcome the limitations of each individual approach, such as local minima and slow convergence, respectively. The potential of both optimization methods is tested and compared, both independently and jointly, using the several test models and synthetic refraction travel-time date sets that employ the same experimental geometry, wavelength and geometrical characteristics of the model anomalies. Moreover, real data from a crosswell tomographic project for the subsurface mapping of an ancient wall foundation are used for testing the efficiency of the proposed algorithm. The results show that the combined use of both methods can exploit the benefits of each approach, leading to improved final models and producing realistic velocity models, without significantly increasing the required computation time.

Genetic Causes of Syndromic and Non-Syndromic Autism

ERIC Educational Resources Information Center

Caglayan, Ahmet O.

2010-01-01

Aims: Over the past decade, genetic tests have become available for numerous heritable disorders, especially those whose inheritance follows the Mendelian model. Autism spectrum disorders (ASDs) represent a group of developmental disorders with a strong genetic basis. During the past few years, genetic research in ASDs has been successful in…

Engineering strategies for the fermentative production of plant alkaloids in yeast.

PubMed

Trenchard, Isis J; Smolke, Christina D

2015-07-01

Microbial hosts engineered for the biosynthesis of plant natural products offer enormous potential as powerful discovery and production platforms. However, the reconstruction of these complex biosynthetic schemes faces numerous challenges due to the number of enzymatic steps and challenging enzyme classes associated with these pathways, which can lead to issues in metabolic load, pathway specificity, and maintaining flux to desired products. Cytochrome P450 enzymes are prevalent in plant specialized metabolism and are particularly difficult to express heterologously. Here, we describe the reconstruction of the sanguinarine branch of the benzylisoquinoline alkaloid pathway in Saccharomyces cerevisiae, resulting in microbial biosynthesis of protoberberine, protopine, and benzophenanthridine alkaloids through to the end-product sanguinarine, which we demonstrate can be efficiently produced in yeast in the absence of the associated biosynthetic enzyme. We achieved titers of 676 μg/L stylopine, 548 μg/L cis-N-methylstylopine, 252 μg/L protopine, and 80 μg/L sanguinarine from the engineered yeast strains. Through our optimization efforts, we describe genetic and culture strategies supporting the functional expression of multiple plant cytochrome P450 enzymes in the context of a large multi-step pathway. Our results also provided insight into relationships between cytochrome P450 activity and yeast ER physiology. We were able to improve the production of critical intermediates by 32-fold through genetic techniques and an additional 45-fold through culture optimization. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Simulation of the detonation process of an ammonium nitrate based emulsion explosive using the lee-tarver reactive flow model

NASA Astrophysics Data System (ADS)

Ribeiro, José B.; Silva, Cristóvão; Mendes, Ricardo; Plaksin, I.; Campos, Jose

2012-03-01

The use of emulsion explosives [EEx] for processing materials (compaction, welding and forming) requires the ability to perform detailed simulations of its detonation process [DP]. Detailed numerical simulations of the DP of this kind of explosives, characterized by having a finite reaction zone thickness, are thought to be suitably performed using the Lee-Tarver reactive flow model. In this work a real coded genetic algorithm methodology was used to estimate the 15 parameters of the reaction rate equation [RRE] of that model for a particular EEx. This methodology allows, in a single optimization procedure, using only one experimental result and without the need of any starting solution, to seek for the 15 parameters of the RRE that fit the numerical to the experimental results. Mass averaging and the Plate-Gap Model have been used for the determination of the shock data used in the unreacted explosive JWL EoS assessment, and the thermochemical code THOR retrieved the data used in the detonation products JWL EoS assessment. The obtained parameters allow a reasonable description of the experimental data.

[Effect of gene optimization on the expression and purification of HDV small antigen produced by genetic engineering].

PubMed

Ding, Jun-Ying; Meng, Qing-Ling; Guo, Min-Zhuo; Yi, Yao; Su, Qiu-Dong; Lu, Xue-Xin; Qiu, Feng; Bi, Sheng-Li

2012-10-01

To study the effect of gene optimization on the expression and purification of HDV small antigen produced by genetic engineering. Based on the colon preference of E. coli, the HDV small antigen original gene from GenBank was optimized. Both the original gene and the optimized gene expressed in prokaryotic cells, SDS-PAGE was made to analyze the protein expression yield and to decide which protein expression style was more proportion than the other. Furthermore, two antigens were purified by chromatography in order to compare the purity by SDS-PAGE and Image Lab software. SDS-PAGE indicated that the molecular weight of target proteins from two groups were the same as we expected. Gene optimization resulted in the higher yield and it could make the product more soluble. After chromatography, the purity of target protein from optimized gene was up to 96.3%, obviously purer than that from original gene. Gene optimization could increase the protein expression yield and solubility of genetic engineering HDV small antigen. In addition, the product from the optimized gene group was easier to be purified for diagnosis usage.

Composite Structure Optimization with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Deslandes, Olivier

2014-06-01

In the frame of optimization studies in CNES launcher directorate structure, thermic and material department, the need of an optimization tool based on metaheuristic and finite element models for composite structural dimensioning was underlined.Indeed, composite structures need complex optimization methodologies in order to be really compared to metallic structures with regard to mass, static strength and stiffness constraints (metallic structures using optimization methods better known).After some bibliography research, the use of a genetic algorithm coupled with design of experiment to generate the initial population was chosen. Academic functions were used to validate the optimization process and then it was applied to an industrial study aiming to optimize an interstage skirt with regard to its mass, stiffness and stability (global buckling).

Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI

2006-07-01

This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for themore » refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)« less

Optimal sensor placement for spatial lattice structure based on genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Wei; Gao, Wei-cheng; Sun, Yi; Xu, Min-jian

2008-10-01

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.

Genetic algorithm optimization of transcutaneous energy transmission systems for implantable ventricular assist devices.

PubMed

Byron, Kelly; Bluvshtein, Vlad; Lucke, Lori

2013-01-01

Transcutaneous energy transmission systems (TETS) wirelessly transmit power through the skin. TETS is particularly desirable for ventricular assist devices (VAD), which currently require cables through the skin to power the implanted pump. Optimizing the inductive link of the TET system is a multi-parameter problem. Most current techniques to optimize the design simplify the problem by combining parameters leading to sub-optimal solutions. In this paper we present an optimization method using a genetic algorithm to handle a larger set of parameters, which leads to a more optimal design. Using this approach, we were able to increase efficiency while also reducing power variability in a prototype, compared to a traditional manual design method.

Numerical solution of a conspicuous consumption model with constant control delay☆

PubMed Central

Huschto, Tony; Feichtinger, Gustav; Hartl, Richard F.; Kort, Peter M.; Sager, Sebastian; Seidl, Andrea

2011-01-01

We derive optimal pricing strategies for conspicuous consumption products in periods of recession. To that end, we formulate and investigate a two-stage economic optimal control problem that takes uncertainty of the recession period length and delay effects of the pricing strategy into account. This non-standard optimal control problem is difficult to solve analytically, and solutions depend on the variable model parameters. Therefore, we use a numerical result-driven approach. We propose a structure-exploiting direct method for optimal control to solve this challenging optimization problem. In particular, we discretize the uncertainties in the model formulation by using scenario trees and target the control delays by introduction of slack control functions. Numerical results illustrate the validity of our approach and show the impact of uncertainties and delay effects on optimal economic strategies. During the recession, delayed optimal prices are higher than the non-delayed ones. In the normal economic period, however, this effect is reversed and optimal prices with a delayed impact are smaller compared to the non-delayed case. PMID:22267871

Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Pak, Chan-gi; Li, Wesley

2009-01-01

Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) to automate analysis and design process by leveraging existing tools to enable true multidisciplinary optimization in the preliminary design stage of subsonic, transonic, supersonic, and hypersonic aircraft. This is a promising technology, but faces many challenges in large-scale, real-world application. This report describes current approaches, recent results, and challenges for multidisciplinary design, analysis, and optimization as demonstrated by experience with the Ikhana fire pod design.!

Numerical difficulties associated with using equality constraints to achieve multi-level decomposition in structural optimization

NASA Technical Reports Server (NTRS)

Thareja, R.; Haftka, R. T.

1986-01-01

There has been recent interest in multidisciplinary multilevel optimization applied to large engineering systems. The usual approach is to divide the system into a hierarchy of subsystems with ever increasing detail in the analysis focus. Equality constraints are usually placed on various design quantities at every successive level to ensure consistency between levels. In many previous applications these equality constraints were eliminated by reducing the number of design variables. In complex systems this may not be possible and these equality constraints may have to be retained in the optimization process. In this paper the impact of such a retention is examined for a simple portal frame problem. It is shown that the equality constraints introduce numerical difficulties, and that the numerical solution becomes very sensitive to optimization parameters for a wide range of optimization algorithms.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Two-level optimization of composite wing structures based on panel genetic optimization

NASA Astrophysics Data System (ADS)

Liu, Boyang

The design of complex composite structures used in aerospace or automotive vehicles presents a major challenge in terms of computational cost. Discrete choices for ply thicknesses and ply angles leads to a combinatorial optimization problem that is too expensive to solve with presently available computational resources. We developed the following methodology for handling this problem for wing structural design: we used a two-level optimization approach with response-surface approximations to optimize panel failure loads for the upper-level wing optimization. We tailored efficient permutation genetic algorithms to the panel stacking sequence design on the lower level. We also developed approach for improving continuity of ply stacking sequences among adjacent panels. The decomposition approach led to a lower-level optimization of stacking sequence with a given number of plies in each orientation. An efficient permutation genetic algorithm (GA) was developed for handling this problem. We demonstrated through examples that the permutation GAs are more efficient for stacking sequence optimization than a standard GA. Repair strategies for standard GA and the permutation GAs for dealing with constraints were also developed. The repair strategies can significantly reduce computation costs for both standard GA and permutation GA. A two-level optimization procedure for composite wing design subject to strength and buckling constraints is presented. At wing-level design, continuous optimization of ply thicknesses with orientations of 0°, 90°, and +/-45° is performed to minimize weight. At the panel level, the number of plies of each orientation (rounded to integers) and inplane loads are specified, and a permutation genetic algorithm is used to optimize the stacking sequence. The process begins with many panel genetic optimizations for a range of loads and numbers of plies of each orientation. Next, a cubic polynomial response surface is fitted to the optimum buckling load. The resulting response surface is used for wing-level optimization. In general, complex composite structures consist of several laminates. A common problem in the design of such structures is that some plies in the adjacent laminates terminate in the boundary between the laminates. These discontinuities may cause stress concentrations and may increase manufacturing difficulty and cost. We developed measures of continuity of two adjacent laminates. We studied tradeoffs between weight and continuity through a simple composite wing design. Finally, we compared the two-level optimization to a single-level optimization based on flexural lamination parameters. The single-level optimization is efficient and feasible for a wing consisting of unstiffened panels.

Software For Genetic Algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steve E.

1992-01-01

SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy.

PubMed

Xing, KeYi; Han, LiBin; Zhou, MengChu; Wang, Feng

2012-06-01

Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

Design and Analysis of an Axisymmetric Phased Array Fed Gregorian Reflector System for Limited Scanning

DTIC Science & Technology

2016-01-22

Numerical electromagnetic simulations based on the multilevel fast multipole method (MLFMM) were used to analyze and optimize the antenna...and are not necessarily endorsed by the United States Government. numerical simulations with the multilevel fast multipole method (MLFMM...and optimized using numerical simulations conducted with the multilevel fast multipole method (MLFMM) using FEKO software (www.feko.info). The

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

Optimization of the Bridgman crystal growth process

NASA Astrophysics Data System (ADS)

Margulies, M.; Witomski, P.; Duffar, T.

2004-05-01

A numerical optimization method of the vertical Bridgman growth configuration is presented and developed. It permits to optimize the furnace temperature field and the pulling rate versus time in order to decrease the radial thermal gradients in the sample. Some constraints are also included in order to insure physically realistic results. The model includes the two classical non-linearities associated to crystal growth processes, the radiative thermal exchange and the release of latent heat at the solid-liquid interface. The mathematical analysis and development of the problem is shortly described. On some examples, it is shown that the method works in a satisfactory way; however the results are dependent on the numerical parameters. Improvements of the optimization model, on the physical and numerical point of view, are suggested.

Stochastic search in structural optimization - Genetic algorithms and simulated annealing

NASA Technical Reports Server (NTRS)

Hajela, Prabhat

1993-01-01

An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Unscented Sampling Techniques For Evolutionary Computation With Applications To Astrodynamic Optimization

DTIC Science & Technology

2016-09-01

to both genetic algorithms and evolution strategies to achieve these goals. The results of this research offer a promising new set of modified ...abs_all.jsp?arnumber=203904 [163] Z. Michalewicz, C. Z. Janikow, and J. B. Krawczyk, “A modified genetic algo- rithm for optimal control problems...Available: http://arc.aiaa.org/doi/abs/10.2514/ 2.7053 375 [166] N. Yokoyama and S. Suzuki, “ Modified genetic algorithm for constrained trajectory

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Crossley, William A.; Martin. Eric T.

2002-01-01

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Reliable numerical computation in an optimal output-feedback design

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm is presented for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters. The algorithm is a part of a design algorithm for optimal linear dynamic output-feedback controller that minimizes a finite-time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control-law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed-loop eigensystem. This approach through the use of an accurate Pade series approximation does not require the closed-loop system matrix to be diagonalizable. The algorithm was included in a control design package for optimal robust low-order controllers. Usefulness of the proposed numerical algorithm was demonstrated using numerous practical design cases where degeneracies occur frequently in the closed-loop system under an arbitrary controller design initialization and during the numerical search.

Genetic and environmental factors affecting early rooting of six Populus genomic groups: implications for tree improvement

Treesearch

Ronald S., Jr. Zalesny

2006-01-01

Genetic and environmental factors affect the early rooting of Populus planted as unrooted hardwood cuttings. Populus genotypes of six genomic groups were tested in numerous studies for the quantitative genetics of rooting, along with effects of preplanting treatments and soil temperature. Genetics data (e.g. heritabilities,...

Study on validation method for femur finite element model under multiple loading conditions

NASA Astrophysics Data System (ADS)

Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu

2018-03-01

Acquisition of accurate and reliable constitutive parameters related to bio-tissue materials was beneficial to improve biological fidelity of a Finite Element (FE) model and predict impact damages more effectively. In this paper, a femur FE model was established under multiple loading conditions with diverse impact positions. Then, based on sequential response surface method and genetic algorithms, the material parameters identification was transformed to a multi-response optimization problem. Finally, the simulation results successfully coincided with force-displacement curves obtained by numerous experiments. Thus, computational accuracy and efficiency of the entire inverse calculation process were enhanced. This method was able to effectively reduce the computation time in the inverse process of material parameters. Meanwhile, the material parameters obtained by the proposed method achieved higher accuracy.

Finding undetected protein associations in cell signaling by belief propagation.

PubMed

Bailly-Bechet, M; Borgs, C; Braunstein, A; Chayes, J; Dagkessamanskaia, A; François, J-M; Zecchina, R

2011-01-11

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.

Transversely polarized sub-diffraction optical needle with ultra-long depth of focus

NASA Astrophysics Data System (ADS)

Guan, Jian; Lin, Jie; Chen, Chen; Ma, Yuan; Tan, Jiubin; Jin, Peng

2017-12-01

We generated purely transversely polarized sub-diffraction optical needles with ultra-long depth of focus (DOF) by focusing azimuthally polarized (AP) beams that were modulated by a vortex 0-2 π phase plate and binary phase diffraction optical elements (DOEs). The concentric belts' radii of the DOEs were optimized by a hybrid genetic particle swarm optimization (HGPSO) algorithm. For the focusing system with the numerical aperture (NA) of 0.95, an optical needle with the full width at half maximum (FWHM) of 0.40 λ and the DOF of 6.23 λ was generated. Similar optical needles were also generated by binary phase DOEs with different belts. The results demonstrated that the binary phase DOEs could achieve smaller FWHMs and longer DOFs simultaneously. The generated needles were circularly polarized on the z-axis and there were no longitudinally polarized components in the focal fields. The radius fabrication errors of a DOE have little effect on the optical needle produced by itself. The generated optical needles can be applied to the fields of photolithography, high-density optical data storage, microscope imaging and particle trapping.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Landslide Kinematical Analysis through Inverse Numerical Modelling and Differential SAR Interferometry

NASA Astrophysics Data System (ADS)

Castaldo, R.; Tizzani, P.; Lollino, P.; Calò, F.; Ardizzone, F.; Lanari, R.; Guzzetti, F.; Manunta, M.

2015-11-01

The aim of this paper is to propose a methodology to perform inverse numerical modelling of slow landslides that combines the potentialities of both numerical approaches and well-known remote-sensing satellite techniques. In particular, through an optimization procedure based on a genetic algorithm, we minimize, with respect to a proper penalty function, the difference between the modelled displacement field and differential synthetic aperture radar interferometry (DInSAR) deformation time series. The proposed methodology allows us to automatically search for the physical parameters that characterize the landslide behaviour. To validate the presented approach, we focus our analysis on the slow Ivancich landslide (Assisi, central Italy). The kinematical evolution of the unstable slope is investigated via long-term DInSAR analysis, by exploiting about 20 years of ERS-1/2 and ENVISAT satellite acquisitions. The landslide is driven by the presence of a shear band, whose behaviour is simulated through a two-dimensional time-dependent finite element model, in two different physical scenarios, i.e. Newtonian viscous flow and a deviatoric creep model. Comparison between the model results and DInSAR measurements reveals that the deviatoric creep model is more suitable to describe the kinematical evolution of the landslide. This finding is also confirmed by comparing the model results with the available independent inclinometer measurements. Our analysis emphasizes that integration of different data, within inverse numerical models, allows deep investigation of the kinematical behaviour of slow active landslides and discrimination of the driving forces that govern their deformation processes.

Numerical research of the optimal control problem in the semi-Markov inventory model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gorshenin, Andrey K.; Belousov, Vasily V.; Shnourkoff, Peter V.

2015-03-10

This paper is devoted to the numerical simulation of stochastic system for inventory management products using controlled semi-Markov process. The results of a special software for the system’s research and finding the optimal control are presented.

OPTIMIZATION OF COUNTERCURRENT STAGED PROCESSES.

DTIC Science & Technology

CHEMICAL ENGINEERING , OPTIMIZATION), (*DISTILLATION, OPTIMIZATION), INDUSTRIAL PRODUCTION, INDUSTRIAL EQUIPMENT, MATHEMATICAL MODELS, DIFFERENCE EQUATIONS, NONLINEAR PROGRAMMING, BOUNDARY VALUE PROBLEMS, NUMERICAL INTEGRATION

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation.

PubMed

Cheema, Jitender Jit Singh; Sankpal, Narendra V; Tambe, Sanjeev S; Kulkarni, Bhaskar D

2002-01-01

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

Steady-state configuration and tension calculations of marine cables under complex currents via separated particle swarm optimization

NASA Astrophysics Data System (ADS)

Xu, Xue-song

2014-12-01

Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton-Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

Genetic Evolution of Shape-Altering Programs for Supersonic Aerodynamics

NASA Technical Reports Server (NTRS)

Kennelly, Robert A., Jr.; Bencze, Daniel P. (Technical Monitor)

2002-01-01

Two constrained shape optimization problems relevant to aerodynamics are solved by genetic programming, in which a population of computer programs evolves automatically under pressure of fitness-driven reproduction and genetic crossover. Known optimal solutions are recovered using a small, naive set of elementary operations. Effectiveness is improved through use of automatically defined functions, especially when one of them is capable of a variable number of iterations, even though the test problems lack obvious exploitable regularities. An attempt at evolving new elementary operations was only partially successful.

Vortex generator design for aircraft inlet distortion as a numerical optimization problem

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Levy, Ralph

1991-01-01

Aerodynamic compatibility of aircraft/inlet/engine systems is a difficult design problem for aircraft that must operate in many different flight regimes. Takeoff, subsonic cruise, supersonic cruise, transonic maneuvering, and high altitude loiter each place different constraints on inlet design. Vortex generators, small wing like sections mounted on the inside surfaces of the inlet duct, are used to control flow separation and engine face distortion. The design of vortex generator installations in an inlet is defined as a problem addressable by numerical optimization techniques. A performance parameter is suggested to account for both inlet distortion and total pressure loss at a series of design flight conditions. The resulting optimization problem is difficult since some of the design parameters take on integer values. If numerical procedures could be used to reduce multimillion dollar development test programs to a small set of verification tests, numerical optimization could have a significant impact on both cost and elapsed time to design new aircraft.

Weighted sum of gray gases model optimization for numerical investigations of processes inside pulverized coal-fired furnaces

NASA Astrophysics Data System (ADS)

Crnomarkovic, Nenad; Belosevic, Srdjan; Tomanovic, Ivan; Milicevic, Aleksandar

2017-12-01

The effects of the number of significant figures (NSF) in the interpolation polynomial coefficients (IPCs) of the weighted sum of gray gases model (WSGM) on results of numerical investigations and WSGM optimization were investigated. The investigation was conducted using numerical simulations of the processes inside a pulverized coal-fired furnace. The radiative properties of the gas phase were determined using the simple gray gas model (SG), two-term WSGM (W2), and three-term WSGM (W3). Ten sets of the IPCs with the same NSF were formed for every weighting coefficient in both W2 and W3. The average and maximal relative difference values of the flame temperatures, wall temperatures, and wall heat fluxes were determined. The investigation showed that the results of numerical investigations were affected by the NSF unless it exceeded certain value. The increase in the NSF did not necessarily lead to WSGM optimization. The combination of the NSF (CNSF) was the necessary requirement for WSGM optimization.

Synthesis design of artificial magnetic metamaterials using a genetic algorithm.

PubMed

Chen, P Y; Chen, C H; Wang, H; Tsai, J H; Ni, W X

2008-08-18

In this article, we present a genetic algorithm (GA) as one branch of artificial intelligence (AI) for the optimization-design of the artificial magnetic metamaterial whose structure is automatically generated by computer through the filling element methodology. A representative design example, metamaterials with permeability of negative unity, is investigated and the optimized structures found by the GA are presented. It is also demonstrated that our approach is effective for the synthesis of functional magnetic and electric metamaterials with optimal structures. This GA-based optimization-design technique shows great versatility and applicability in the design of functional metamaterials.

Improved alignment evaluation and optimization : final report.

DOT National Transportation Integrated Search

2007-09-11

This report outlines the development of an enhanced highway alignment evaluation and optimization : model. A GIS-based software tool is prepared for alignment optimization that uses genetic algorithms for : optimal search. The software is capable of ...

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Threshold matrix for digital halftoning by genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero

1998-10-01

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

AI/OR computational model for integrating qualitative and quantitative design methods

NASA Technical Reports Server (NTRS)

Agogino, Alice M.; Bradley, Stephen R.; Cagan, Jonathan; Jain, Pramod; Michelena, Nestor

1990-01-01

A theoretical framework for integrating qualitative and numerical computational methods for optimally-directed design is described. The theory is presented as a computational model and features of implementations are summarized where appropriate. To demonstrate the versatility of the methodology we focus on four seemingly disparate aspects of the design process and their interaction: (1) conceptual design, (2) qualitative optimal design, (3) design innovation, and (4) numerical global optimization.

Dual-mode nested search method for categorical uncertain multi-objective optimization

NASA Astrophysics Data System (ADS)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

Optimal interpolation and the Kalman filter. [for analysis of numerical weather predictions

NASA Technical Reports Server (NTRS)

Cohn, S.; Isaacson, E.; Ghil, M.

1981-01-01

The estimation theory of stochastic-dynamic systems is described and used in a numerical study of optimal interpolation. The general form of data assimilation methods is reviewed. The Kalman-Bucy, KB filter, and optimal interpolation (OI) filters are examined for effectiveness in performance as gain matrices using a one-dimensional form of the shallow-water equations. Control runs in the numerical analyses were performed for a ten-day forecast in concert with the OI method. The effects of optimality, initialization, and assimilation were studied. It was found that correct initialization is necessary in order to localize errors, especially near boundary points. Also, the use of small forecast error growth rates over data-sparse areas was determined to offset inaccurate modeling of correlation functions near boundaries.

Anthropogenic alterations of genetic diversity within tree populations: Implications for forest ecosystem resilience

Treesearch

Paul G. Schaberg; Donald H. DeHayes; Gary J. Hawley; Samuel E. Nijensohn

2008-01-01

Healthy forests provide many of the essential ecosystem services upon which all life depends. Genetic diversity is an essential component of long-term forest health because it provides a basis for adaptation and resilience to environmental stress and change. In addition to natural processes, numerous anthropogenic factors deplete forest genetic resources. Genetic...

Investigation on the use of optimization techniques for helicopter airframe vibrations design studies

NASA Technical Reports Server (NTRS)

Sreekanta Murthy, T.

1992-01-01

Results of the investigation of formal nonlinear programming-based numerical optimization techniques of helicopter airframe vibration reduction are summarized. The objective and constraint function and the sensitivity expressions used in the formulation of airframe vibration optimization problems are presented and discussed. Implementation of a new computational procedure based on MSC/NASTRAN and CONMIN in a computer program system called DYNOPT for optimizing airframes subject to strength, frequency, dynamic response, and dynamic stress constraints is described. An optimization methodology is proposed which is thought to provide a new way of applying formal optimization techniques during the various phases of the airframe design process. Numerical results obtained from the application of the DYNOPT optimization code to a helicopter airframe are discussed.

Fireworks algorithm for mean-VaR/CVaR models

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Liu, Zhifeng

2017-10-01

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.

Morphological evolution of protective works by Genetic Algorithms: An application to Mt Etna

NASA Astrophysics Data System (ADS)

Marocco, Davide; Spataro, William; D'Ambrosio, Donato; Filippone, Giuseppe; Rongo, Rocco; Iovine, Giulio; Neri, Marco

2013-04-01

The hazard induced by dangerous flow-type phenomena - e.g. lava flows, earth flows, debris flows, and debris avalanches - has increased in recent years due to continuous urbanization. In many cases, the numerical simulation of hypothetical events can help to forecast the flow path in advance and therefore give indications about the areas that can be considered for the construction of protective works - e.g. earth barriers or channels. In this way, urbanized areas, as well as cultural heritage sites or even important infrastructures, can be protected by diverting the flow towards lower interest regions. Here, we have considered the numerical Cellular Automata model Sciara-fv2 for simulating lava flows at Mt Etna and Genetic Algorithms for optimizing the position, orientation and extension of an earth barrier built to protect the Rifugio Sapienza, a well-known touristic facility located near the summit of the volcano. The Rifugio Sapienza area was in fact interested by a lava flow in 2003, which destroyed a Service Center, a parking area and a Cafeteria. In this study, a perimeter was devised around the Rifugio (i.e., security perimeter), which delimitates the area that has to be protected by the flow. Furthermore, another perimeter was devised (i.e., work perimeter), specifying the area in which the earth barrier can be located. The barrier is specified by three parameters, namely the two geographic coordinates of the vertex and the height. In fact, in this preliminary analysis the barrier was modeled as a segment (in plant) having a constant height. Though preliminary, the study has produced extremely positive results. Among different alternatives generated by the genetic algorithm, an interesting scenario consists of a 35 meters barrier high solution, which completely deviates the flow avoiding that the lava reaches the inhabited area. The relative elevated height of the barrier is high due to the fact that the crater is located close to the area to be protected and, consequently, the lava rate is very high.

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

NASA Astrophysics Data System (ADS)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Self-calibration of a noisy multiple-sensor system with genetic algorithms

NASA Astrophysics Data System (ADS)

Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

1996-01-01

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

Genetics-based control of a mimo boiler-turbine plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Topology Optimization - Engineering Contribution to Architectural Design

NASA Astrophysics Data System (ADS)

Tajs-Zielińska, Katarzyna; Bochenek, Bogdan

2017-10-01

The idea of the topology optimization is to find within a considered design domain the distribution of material that is optimal in some sense. Material, during optimization process, is redistributed and parts that are not necessary from objective point of view are removed. The result is a solid/void structure, for which an objective function is minimized. This paper presents an application of topology optimization to multi-material structures. The design domain defined by shape of a structure is divided into sub-regions, for which different materials are assigned. During design process material is relocated, but only within selected region. The proposed idea has been inspired by architectural designs like multi-material facades of buildings. The effectiveness of topology optimization is determined by proper choice of numerical optimization algorithm. This paper utilises very efficient heuristic method called Cellular Automata. Cellular Automata are mathematical, discrete idealization of a physical systems. Engineering implementation of Cellular Automata requires decomposition of the design domain into a uniform lattice of cells. It is assumed, that the interaction between cells takes place only within the neighbouring cells. The interaction is governed by simple, local update rules, which are based on heuristics or physical laws. The numerical studies show, that this method can be attractive alternative to traditional gradient-based algorithms. The proposed approach is evaluated by selected numerical examples of multi-material bridge structures, for which various material configurations are examined. The numerical studies demonstrated a significant influence the material sub-regions location on the final topologies. The influence of assumed volume fraction on final topologies for multi-material structures is also observed and discussed. The results of numerical calculations show, that this approach produces different results as compared with classical one-material problems.

Common aero vehicle autonomous reentry trajectory optimization satisfying waypoint and no-fly zone constraints

NASA Astrophysics Data System (ADS)

Jorris, Timothy R.

2007-12-01

To support the Air Force's Global Reach concept, a Common Aero Vehicle is being designed to support the Global Strike mission. "Waypoints" are specified for reconnaissance or multiple payload deployments and "no-fly zones" are specified for geopolitical restrictions or threat avoidance. Due to time critical targets and multiple scenario analysis, an autonomous solution is preferred over a time-intensive, manually iterative one. Thus, a real-time or near real-time autonomous trajectory optimization technique is presented to minimize the flight time, satisfy terminal and intermediate constraints, and remain within the specified vehicle heating and control limitations. This research uses the Hypersonic Cruise Vehicle (HCV) as a simplified two-dimensional platform to compare multiple solution techniques. The solution techniques include a unique geometric approach developed herein, a derived analytical dynamic optimization technique, and a rapidly emerging collocation numerical approach. This up-and-coming numerical technique is a direct solution method involving discretization then dualization, with pseudospectral methods and nonlinear programming used to converge to the optimal solution. This numerical approach is applied to the Common Aero Vehicle (CAV) as the test platform for the full three-dimensional reentry trajectory optimization problem. The culmination of this research is the verification of the optimality of this proposed numerical technique, as shown for both the two-dimensional and three-dimensional models. Additionally, user implementation strategies are presented to improve accuracy and enhance solution convergence. Thus, the contributions of this research are the geometric approach, the user implementation strategies, and the determination and verification of a numerical solution technique for the optimal reentry trajectory problem that minimizes time to target while satisfying vehicle dynamics and control limitation, and heating, waypoint, and no-fly zone constraints.

The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization.

PubMed

Błażej, Paweł; Wnȩtrzak, Małgorzata; Mackiewicz, Paweł

2016-12-01

One of theories explaining the present structure of canonical genetic code assumes that it was optimized to minimize harmful effects of amino acid replacements resulting from nucleotide substitutions and translational errors. A way to testify this concept is to find the optimal code under given criteria and compare it with the canonical genetic code. Unfortunately, the huge number of possible alternatives makes it impossible to find the optimal code using exhaustive methods in sensible time. Therefore, heuristic methods should be applied to search the space of possible solutions. Evolutionary algorithms (EA) seem to be ones of such promising approaches. This class of methods is founded both on mutation and crossover operators, which are responsible for creating and maintaining the diversity of candidate solutions. These operators possess dissimilar characteristics and consequently play different roles in the process of finding the best solutions under given criteria. Therefore, the effective searching for the potential solutions can be improved by applying both of them, especially when these operators are devised specifically for a given problem. To study this subject, we analyze the effectiveness of algorithms for various combinations of mutation and crossover probabilities under three models of the genetic code assuming different restrictions on its structure. To achieve that, we adapt the position based crossover operator for the most restricted model and develop a new type of crossover operator for the more general models. The applied fitness function describes costs of amino acid replacement regarding their polarity. Our results indicate that the usage of crossover operators can significantly improve the quality of the solutions. Moreover, the simulations with the crossover operator optimize the fitness function in the smaller number of generations than simulations without this operator. The optimal genetic codes without restrictions on their structure minimize the costs about 2.7 times better than the canonical genetic code. Interestingly, the optimal codes are dominated by amino acids characterized by polarity close to its average value for all amino acids. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comparison of Structural Optimization Techniques for a Nuclear Electric Space Vehicle

NASA Technical Reports Server (NTRS)

Benford, Andrew

2003-01-01

The purpose of this paper is to utilize the optimization method of genetic algorithms (GA) for truss design on a nuclear propulsion vehicle. Genetic Algorithms are a guided, random search that mirrors Darwin s theory of natural selection and survival of the fittest. To verify the GA s capabilities, other traditional optimization methods were used to compare the results obtained by the GA's, first on simple 2-D structures, and eventually on full-scale 3-D truss designs.

Optimization of Contrast Detection Power with Probabilistic Behavioral Information

PubMed Central

Cordes, Dietmar; Herzmann, Grit; Nandy, Rajesh; Curran, Tim

2012-01-01

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects’ responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data. PMID:22326984

The power of associative learning and the ontogeny of optimal behaviour.

PubMed

Enquist, Magnus; Lind, Johan; Ghirlanda, Stefano

2016-11-01

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce 'intelligent' behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion.

The power of associative learning and the ontogeny of optimal behaviour

PubMed Central

Enquist, Magnus; Lind, Johan

2016-01-01

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce ‘intelligent’ behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion. PMID:28018662

Geometric versus numerical optimal control of a dissipative spin-(1/2) particle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lapert, M.; Sugny, D.; Zhang, Y.

2010-12-15

We analyze the saturation of a nuclear magnetic resonance (NMR) signal using optimal magnetic fields. We consider both the problems of minimizing the duration of the control and its energy for a fixed duration. We solve the optimal control problems by using geometric methods and a purely numerical approach, the grape algorithm, the two methods being based on the application of the Pontryagin maximum principle. A very good agreement is obtained between the two results. The optimal solutions for the energy-minimization problem are finally implemented experimentally with available NMR techniques.

Optimizing snake locomotion on an inclined plane

NASA Astrophysics Data System (ADS)

Wang, Xiaolin; Osborne, Matthew T.; Alben, Silas

2014-01-01

We develop a model to study the locomotion of snakes on inclined planes. We determine numerically which snake motions are optimal for two retrograde traveling-wave body shapes, triangular and sinusoidal waves, across a wide range of frictional parameters and incline angles. In the regime of large transverse friction coefficients, we find power-law scalings for the optimal wave amplitudes and corresponding costs of locomotion. We give an asymptotic analysis to show that the optimal snake motions are traveling waves with amplitudes given by the same scaling laws found in the numerics.

A systematic approach for watershed ecological restoration strategy making: An application in the Taizi River Basin in northern China.

PubMed

Li, Mengdi; Fan, Juntao; Zhang, Yuan; Guo, Fen; Liu, Lusan; Xia, Rui; Xu, Zongxue; Wu, Fengchang

2018-05-15

Aiming to protect freshwater ecosystems, river ecological restoration has been brought into the research spotlight. However, it is challenging for decision makers to set appropriate objectives and select a combination of rehabilitation acts from numerous possible solutions to meet ecological, economic, and social demands. In this study, we developed a systematic approach to help make an optimal strategy for watershed restoration, which incorporated ecological security assessment and multi-objectives optimization (MOO) into the planning process to enhance restoration efficiency and effectiveness. The river ecological security status was evaluated by using a pressure-state-function-response (PSFR) assessment framework, and MOO was achieved by searching for the Pareto optimal solutions via Non-dominated Sorting Genetic Algorithm II (NSGA-II) to balance tradeoffs between different objectives. Further, we clustered the searched solutions into three types in terms of different optimized objective function values in order to provide insightful information for decision makers. The proposed method was applied in an example rehabilitation project in the Taizi River Basin in northern China. The MOO result in the Taizi River presented a set of Pareto optimal solutions that were classified into three types: I - high ecological improvement, high cost and high benefits solution; II - medial ecological improvement, medial cost and medial economic benefits solution; III - low ecological improvement, low cost and low economic benefits solution. The proposed systematic approach in our study can enhance the effectiveness of riverine ecological restoration project and could provide valuable reference for other ecological restoration planning. Copyright © 2018 Elsevier B.V. All rights reserved.

Complete Numerical Solution of the Diffusion Equation of Random Genetic Drift

PubMed Central

Zhao, Lei; Yue, Xingye; Waxman, David

2013-01-01

A numerical method is presented to solve the diffusion equation for the random genetic drift that occurs at a single unlinked locus with two alleles. The method was designed to conserve probability, and the resulting numerical solution represents a probability distribution whose total probability is unity. We describe solutions of the diffusion equation whose total probability is unity as complete. Thus the numerical method introduced in this work produces complete solutions, and such solutions have the property that whenever fixation and loss can occur, they are automatically included within the solution. This feature demonstrates that the diffusion approximation can describe not only internal allele frequencies, but also the boundary frequencies zero and one. The numerical approach presented here constitutes a single inclusive framework from which to perform calculations for random genetic drift. It has a straightforward implementation, allowing it to be applied to a wide variety of problems, including those with time-dependent parameters, such as changing population sizes. As tests and illustrations of the numerical method, it is used to determine: (i) the probability density and time-dependent probability of fixation for a neutral locus in a population of constant size; (ii) the probability of fixation in the presence of selection; and (iii) the probability of fixation in the presence of selection and demographic change, the latter in the form of a changing population size. PMID:23749318

Three-dimensional shape optimization of a cemented hip stem and experimental validations.

PubMed

Higa, Masaru; Tanino, Hiromasa; Nishimura, Ikuya; Mitamura, Yoshinori; Matsuno, Takeo; Ito, Hiroshi

2015-03-01

This study proposes novel optimized stem geometry with low stress values in the cement using a finite element (FE) analysis combined with an optimization procedure and experimental measurements of cement stress in vitro. We first optimized an existing stem geometry using a three-dimensional FE analysis combined with a shape optimization technique. One of the most important factors in the cemented stem design is to reduce stress in the cement. Hence, in the optimization study, we minimized the largest tensile principal stress in the cement mantle under a physiological loading condition by changing the stem geometry. As the next step, the optimized stem and the existing stem were manufactured to validate the usefulness of the numerical models and the results of the optimization in vitro. In the experimental study, strain gauges were embedded in the cement mantle to measure the strain in the cement mantle adjacent to the stems. The overall trend of the experimental study was in good agreement with the results of the numerical study, and we were able to reduce the largest stress by more than 50% in both shape optimization and strain gauge measurements. Thus, we could validate the usefulness of the numerical models and the results of the optimization using the experimental models. The optimization employed in this study is a useful approach for developing new stem designs.

[The genetic fingerprints file in France: between security and freedom].

PubMed

Manaouil, C; Gignon, M; Werbrouck, A; Jarde, O

2008-01-01

In France, the French National File Automated with Genetic fingerprints (FNAEG) is a bank automated by genetic data which is used in penal domain. It facilitates search of the authors of malpractices, or the missing people. Since 1998, it has enabled to resolve numerous criminal cases. An extension of the field of application has been observed. It is a confidential register which is subjected to numerous controls. Nevertheless, private character of the data and its functioning (criminal character of the refusal of taking, periods of answer, and problem of data's conservation) explain the important contesting of associations worried about the respect of personal freedoms.

Promoter library-based module combination (PLMC) technology for optimization of threonine biosynthesis in Corynebacterium glutamicum.

PubMed

Wei, Liang; Xu, Ning; Wang, Yiran; Zhou, Wei; Han, Guoqiang; Ma, Yanhe; Liu, Jun

2018-05-01

Due to the lack of efficient control elements and tools, the fine-tuning of gene expression in the multi-gene metabolic pathways is still a great challenge for engineering microbial cell factories, especially for the important industrial microorganism Corynebacterium glutamicum. In this study, the promoter library-based module combination (PLMC) technology was developed to efficiently optimize the expression of genes in C. glutamicum. A random promoter library was designed to contain the putative - 10 (NNTANANT) and - 35 (NNGNCN) consensus motifs, and refined through a three-step screening procedure to achieve numerous genetic control elements with different strength levels, including fluorescence-activated cell sorting (FACS) screening, agar plate screening, and 96-well plate screening. Multiple conventional strategies were employed for further precise characterizations of the promoter library, such as real-time quantitative PCR, sodium dodecyl sulfate polyacrylamide gel electrophoresis, FACS analysis, and the lacZ reporter system. These results suggested that the established promoter elements effectively regulated gene expression and showed varying strengths over a wide range. Subsequently, a multi-module combination technology was created based on the efficient promoter elements for combination and optimization of modules in the multi-gene pathways. Using this technology, the threonine biosynthesis pathway was reconstructed and optimized by predictable tuning expression of five modules in C. glutamicum. The threonine titer of the optimized strain was significantly improved to 12.8 g/L, an approximate 6.1-fold higher than that of the control strain. Overall, the PLMC technology presented in this study provides a rapid and effective method for combination and optimization of multi-gene pathways in C. glutamicum.

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Shehadeh, Hana; Lea, Robert N.

1992-01-01

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

A genetic algorithm solution to the unit commitment problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V.

1996-02-01

This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 unitsmore » and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.« less

Genetic Modification in Dedicated Bioenergy Crops and Strategies for Gene Confinement

USDA-ARS?s Scientific Manuscript database

Genetic modification of dedicated bioenergy crops is in its infancy; however, there are numerous advantages to the use of these tools to improve crops used for biofuels. Potential improved traits through genetic engineering (GE) include herbicide resistance, pest, drought, cold and salt tolerance, l...

Incorporating a Discussion of Genetic Attributions into Psychology Courses

ERIC Educational Resources Information Center

Sheldon, Jane P.

2018-01-01

A highly relevant construct that may need more emphasis in our psychology courses is genetic attributions. Attributions are causal explanations people make for specific behaviors, characteristics, occurrences, or differences. Research has found that genetic attributions are related to numerous biopsychosocial phenomena (e.g., perceptions about…

A staggered-grid finite-difference scheme optimized in the time–space domain for modeling scalar-wave propagation in geophysical problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Sirui, E-mail: siruitan@hotmail.com; Huang, Lianjie, E-mail: ljh@lanl.gov

For modeling scalar-wave propagation in geophysical problems using finite-difference schemes, optimizing the coefficients of the finite-difference operators can reduce numerical dispersion. Most optimized finite-difference schemes for modeling seismic-wave propagation suppress only spatial but not temporal dispersion errors. We develop a novel optimized finite-difference scheme for numerical scalar-wave modeling to control dispersion errors not only in space but also in time. Our optimized scheme is based on a new stencil that contains a few more grid points than the standard stencil. We design an objective function for minimizing relative errors of phase velocities of waves propagating in all directions within amore » given range of wavenumbers. Dispersion analysis and numerical examples demonstrate that our optimized finite-difference scheme is computationally up to 2.5 times faster than the optimized schemes using the standard stencil to achieve the similar modeling accuracy for a given 2D or 3D problem. Compared with the high-order finite-difference scheme using the same new stencil, our optimized scheme reduces 50 percent of the computational cost to achieve the similar modeling accuracy. This new optimized finite-difference scheme is particularly useful for large-scale 3D scalar-wave modeling and inversion.« less

Computational Efficiency of the Simplex Embedding Method in Convex Nondifferentiable Optimization

NASA Astrophysics Data System (ADS)

Kolosnitsyn, A. V.

2018-02-01

The simplex embedding method for solving convex nondifferentiable optimization problems is considered. A description of modifications of this method based on a shift of the cutting plane intended for cutting off the maximum number of simplex vertices is given. These modification speed up the problem solution. A numerical comparison of the efficiency of the proposed modifications based on the numerical solution of benchmark convex nondifferentiable optimization problems is presented.

Multidisciplinary design optimization using genetic algorithms

NASA Technical Reports Server (NTRS)

Unal, Resit

1994-01-01

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Optimizing DNA assembly based on statistical language modelling.

PubMed

Fang, Gang; Zhang, Shemin; Dong, Yafei

2017-12-15

By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

A satellite constellation optimization for a regional GNSS remote sensing mission

NASA Astrophysics Data System (ADS)

Gavili Kilaneh, Narin; Mashhadi Hossainali, Masoud

2017-04-01

Due to the recent advances in the Global Navigation Satellite System Remote sensing (GNSS¬R) applications, optimization of a satellite orbit to investigate the Earth's properties seems significant. The comparison of the GNSS direct and reflected signals received by a Low Earth Orbit (LEO) satellite introduces a new technique to remotely sense the Earth. Several GNSS¬R missions including Cyclone Global Navigation Satellite System (CYGNSS) have been proposed for different applications such as the ocean wind speed and height monitoring. The geometric optimization of the satellite orbit before starting the mission is a key step for every space mission. Since satellite constellation design varies depending on the application, we have focused on the required geometric criteria for oceanography applications in a specified region. Here, the total number of specular points, their spatial distribution and the accuracy of their position are assumed to be sufficient for oceanography applications. Gleason's method is used to determine the position of specular points. We considered the 2-D lattice and 3-D lattice theory of flower constellation to survey whether a circular orbit or an elliptical one is suitable to improve the solution. Genetic algorithm is implemented to solve the problem. To check the visibility condition between the LEO and GPS satellites, the satellite initial state is propagated by a variable step size numerical integration method. Constellation orbit parameters achieved by optimization provide a better resolution and precession for the specular points in the study area of this research.

Using genetic algorithms to determine near-optimal pricing, investment and operating strategies in the electric power industry

NASA Astrophysics Data System (ADS)

Wu, Dongjun

Network industries have technologies characterized by a spatial hierarchy, the "network," with capital-intensive interconnections and time-dependent, capacity-limited flows of products and services through the network to customers. This dissertation studies service pricing, investment and business operating strategies for the electric power network. First-best solutions for a variety of pricing and investment problems have been studied. The evaluation of genetic algorithms (GA, which are methods based on the idea of natural evolution) as a primary means of solving complicated network problems, both w.r.t. pricing: as well as w.r.t. investment and other operating decisions, has been conducted. New constraint-handling techniques in GAs have been studied and tested. The actual application of such constraint-handling techniques in solving practical non-linear optimization problems has been tested on several complex network design problems with encouraging initial results. Genetic algorithms provide solutions that are feasible and close to optimal when the optimal solution is know; in some instances, the near-optimal solutions for small problems by the proposed GA approach can only be tested by pushing the limits of currently available non-linear optimization software. The performance is far better than several commercially available GA programs, which are generally inadequate in solving any of the problems studied in this dissertation, primarily because of their poor handling of constraints. Genetic algorithms, if carefully designed, seem very promising in solving difficult problems which are intractable by traditional analytic methods.

Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers

NASA Technical Reports Server (NTRS)

Patera, Anthony T.; Patera, Anthony

1993-01-01

Although the advent of fast and inexpensive parallel computers has rendered numerous previously intractable calculations feasible, many numerical simulations remain too resource-intensive to be directly inserted in engineering optimization efforts. An attractive alternative to direct insertion considers models for computational systems: the expensive simulation is evoked only to construct and validate a simplified, input-output model; this simplified input-output model then serves as a simulation surrogate in subsequent engineering optimization studies. A simple 'Bayesian-validated' statistical framework for the construction, validation, and purposive application of static computer simulation surrogates is presented. As an example, dissipation-transport optimization of laminar-flow eddy-promoter heat exchangers are considered: parallel spectral element Navier-Stokes calculations serve to construct and validate surrogates for the flowrate and Nusselt number; these surrogates then represent the originating Navier-Stokes equations in the ensuing design process.

A Numerical-Analytical Approach Based on Canonical Transformations for Computing Optimal Low-Thrust Transfers

NASA Astrophysics Data System (ADS)

da Silva Fernandes, S.; das Chagas Carvalho, F.; Bateli Romão, J. V.

2018-04-01

A numerical-analytical procedure based on infinitesimal canonical transformations is developed for computing optimal time-fixed low-thrust limited power transfers (no rendezvous) between coplanar orbits with small eccentricities in an inverse-square force field. The optimization problem is formulated as a Mayer problem with a set of non-singular orbital elements as state variables. Second order terms in eccentricity are considered in the development of the maximum Hamiltonian describing the optimal trajectories. The two-point boundary value problem of going from an initial orbit to a final orbit is solved by means of a two-stage Newton-Raphson algorithm which uses an infinitesimal canonical transformation. Numerical results are presented for some transfers between circular orbits with moderate radius ratio, including a preliminary analysis of Earth-Mars and Earth-Venus missions.

Novel Numerical Methods for Optimal Control Problems Involving Fractional-Order Differential Equations

DTIC Science & Technology

2018-03-14

pricing, Appl. Math . Comp. Vol.305, 174-187 (2017) 5. W. Li, S. Wang, Pricing European options with proportional transaction costs and stochastic...for fractional differential equation. Numer. Math . Theor. Methods Appl. 5, 229–241, 2012. [23] Kilbas A.A. and Marzan, S.A., Cauchy problem for...numerical technique for solving fractional optimal control problems, Comput. Math . Appl., 62, Issue 3, 1055–1067, 2011. [26] Lotfi A., Yousefi SA., Dehghan M

Biotechnology; Managing the Risks of Field Testing Genetically Engineered Organisms

DTIC Science & Technology

1988-06-01

jurisdictional lines. The agencies’ principal regulatory tool for managing the risks of field testing genetically engineered organisms is the authority to...Regulatory authority has been established in numerous federal statutes designed to prevent the occurrence of harm to the environment and public health...would not be reviewed or regulated at all. According to the Insti- tute, "there are numerous organisms outside the confines of t he plant ST~( Iflt l l l

Directed Chemical Evolution with an Outsized Genetic Code

PubMed Central

Krusemark, Casey J.; Tilmans, Nicolas P.; Brown, Patrick O.; Harbury, Pehr B.

2016-01-01

The first demonstration that macromolecules could be evolved in a test tube was reported twenty-five years ago. That breakthrough meant that billions of years of chance discovery and refinement could be compressed into a few weeks, and provided a powerful tool that now dominates all aspects of protein engineering. A challenge has been to extend this scientific advance into synthetic chemical space: to enable the directed evolution of abiotic molecules. The problem has been tackled in many ways. These include expanding the natural genetic code to include unnatural amino acids, engineering polyketide and polypeptide synthases to produce novel products, and tagging combinatorial chemistry libraries with DNA. Importantly, there is still no small-molecule analog of directed protein evolution, i.e. a substantiated approach for optimizing complex (≥ 10^9 diversity) populations of synthetic small molecules over successive generations. We present a key advance towards this goal: a tool for genetically-programmed synthesis of small-molecule libraries from large chemical alphabets. The approach accommodates alphabets that are one to two orders of magnitude larger than any in Nature, and facilitates evolution within the chemical spaces they create. This is critical for small molecules, which are built up from numerous and highly varied chemical fragments. We report a proof-of-concept chemical evolution experiment utilizing an outsized genetic code, and demonstrate that fitness traits can be passed from an initial small-molecule population through to the great-grandchildren of that population. The results establish the practical feasibility of engineering synthetic small molecules through accelerated evolution. PMID:27508294

Artificial Intelligence-Based Models for the Optimal and Sustainable Use of Groundwater in Coastal Aquifers

NASA Astrophysics Data System (ADS)

Sreekanth, J.; Datta, Bithin

2011-07-01

Overexploitation of the coastal aquifers results in saltwater intrusion. Once saltwater intrusion occurs, it involves huge cost and long-term remediation measures to remediate these contaminated aquifers. Hence, it is important to have strategies for the sustainable use of coastal aquifers. This study develops a methodology for the optimal management of saltwater intrusion prone aquifers. A linked simulation-optimization-based management strategy is developed. The methodology uses genetic-programming-based models for simulating the aquifer processes, which is then linked to a multi-objective genetic algorithm to obtain optimal management strategies in terms of groundwater extraction from potential well locations in the aquifer.

Use of Ensemble Numerical Weather Prediction Data for Inversely Determining Atmospheric Refractivity in Surface Ducting Conditions

NASA Astrophysics Data System (ADS)

Greenway, D. P.; Hackett, E.

2017-12-01

Under certain atmospheric refractivity conditions, propagated electromagnetic waves (EM) can become trapped between the surface and the bottom of the atmosphere's mixed layer, which is referred to as surface duct propagation. Being able to predict the presence of these surface ducts can reap many benefits to users and developers of sensing technologies and communication systems because they significantly influence the performance of these systems. However, the ability to directly measure or model a surface ducting layer is challenging due to the high spatial resolution and large spatial coverage needed to make accurate refractivity estimates for EM propagation; thus, inverse methods have become an increasingly popular way of determining atmospheric refractivity. This study uses data from the Coupled Ocean/Atmosphere Mesoscale Prediction System developed by the Naval Research Laboratory and instrumented helicopter (helo) measurements taken during the Wallops Island Field Experiment to evaluate the use of ensemble forecasts in refractivity inversions. Helo measurements and ensemble forecasts are optimized to a parametric refractivity model, and three experiments are performed to evaluate whether incorporation of ensemble forecast data aids in more timely and accurate inverse solutions using genetic algorithms. The results suggest that using optimized ensemble members as an initial population for the genetic algorithms generally enhances the accuracy and speed of the inverse solution; however, use of the ensemble data to restrict parameter search space yields mixed results. Inaccurate results are related to parameterization of the ensemble members' refractivity profile and the subsequent extraction of the parameter ranges to limit the search space.

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Optimal recombination in genetic algorithms for flowshop scheduling problems

NASA Astrophysics Data System (ADS)

Kovalenko, Julia

2016-10-01

The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.

Solving traveling salesman problems with DNA molecules encoding numerical values.

PubMed

Lee, Ji Youn; Shin, Soo-Yong; Park, Tai Hyun; Zhang, Byoung-Tak

2004-12-01

We introduce a DNA encoding method to represent numerical values and a biased molecular algorithm based on the thermodynamic properties of DNA. DNA strands are designed to encode real values by variation of their melting temperatures. The thermodynamic properties of DNA are used for effective local search of optimal solutions using biochemical techniques, such as denaturation temperature gradient polymerase chain reaction and temperature gradient gel electrophoresis. The proposed method was successfully applied to the traveling salesman problem, an instance of optimization problems on weighted graphs. This work extends the capability of DNA computing to solving numerical optimization problems, which is contrasted with other DNA computing methods focusing on logical problem solving.

A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems

NASA Astrophysics Data System (ADS)

Heinkenschloss, Matthias

2005-01-01

We study a class of time-domain decomposition-based methods for the numerical solution of large-scale linear quadratic optimal control problems. Our methods are based on a multiple shooting reformulation of the linear quadratic optimal control problem as a discrete-time optimal control (DTOC) problem. The optimality conditions for this DTOC problem lead to a linear block tridiagonal system. The diagonal blocks are invertible and are related to the original linear quadratic optimal control problem restricted to smaller time-subintervals. This motivates the application of block Gauss-Seidel (GS)-type methods for the solution of the block tridiagonal systems. Numerical experiments show that the spectral radii of the block GS iteration matrices are larger than one for typical applications, but that the eigenvalues of the iteration matrices decay to zero fast. Hence, while the GS method is not expected to convergence for typical applications, it can be effective as a preconditioner for Krylov-subspace methods. This is confirmed by our numerical tests.A byproduct of this research is the insight that certain instantaneous control techniques can be viewed as the application of one step of the forward block GS method applied to the DTOC optimality system.

Optimal level of inbreeding in the common lizard.

PubMed

Richard, M; Losdat, S; Lecomte, J; de Fraipont, M; Clobert, J

2009-08-07

Mate choice with regard to genetic similarity has been rarely considered as a dynamic process. We examined this possibility in breeding populations of the common lizard (Lacerta vivipara) kept for several years in semi-natural conditions. We investigated whether they displayed a pattern of mate choice according to the genetic similarity and whether it was context-dependent. Mate choice depended on genetic similarity with the partner and also on age and condition. There was no systematic avoidance of inbreeding. Females of intermediate ages, more monogamous, did not mate with genetically similar partners, whereas younger and older females, more polyandrous, did but highest clutch proportions were associated with intermediate values of pair-relatedness. These results indicate dynamic mate choice, suggesting that individuals of different phenotypes select their partners in different ways according to their genetic similarity. We consider our results in the light of diverse and apparently contradictory theories concerning genetic compatibility, and particularly, optimal inbreeding and inclusive fitness.

Simultaneous optimization of loading pattern and burnable poison placement for PWRs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alim, F.; Ivanov, K.; Yilmaz, S.

2006-07-01

To solve in-core fuel management optimization problem, GARCO-PSU (Genetic Algorithm Reactor Core Optimization - Pennsylvania State Univ.) is developed. This code is applicable for all types and geometry of PWR core structures with unlimited number of fuel assembly (FA) types in the inventory. For this reason an innovative genetic algorithm is developed with modifying the classical representation of the genotype. In-core fuel management heuristic rules are introduced into GARCO. The core re-load design optimization has two parts, loading pattern (LP) optimization and burnable poison (BP) placement optimization. These parts depend on each other, but it is difficult to solve themore » combined problem due to its large size. Separating the problem into two parts provides a practical way to solve the problem. However, the result of this method does not reflect the real optimal solution. GARCO-PSU achieves to solve LP optimization and BP placement optimization simultaneously in an efficient manner. (authors)« less

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

PubMed Central

Svečko, Rajko

2014-01-01

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

Distributed query plan generation using multiobjective genetic algorithm.

PubMed

Panicker, Shina; Kumar, T V Vijay

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

PubMed Central

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors

NASA Astrophysics Data System (ADS)

Mayer, Alexandre; Bay, Annick; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

2014-09-01

We present a genetic algorithm (GA) we developed for the optimization of light-emitting diodes (LED) and solar thermal collectors. The surface of a LED can be covered by periodic structures whose geometrical and material parameters must be adjusted in order to maximize the extraction of light. The optimization of these parameters by the GA enabled us to get a light-extraction efficiency η of 11.0% from a GaN LED (for comparison, the flat material has a light-extraction efficiency η of only 3.7%). The solar thermal collector we considered consists of a waffle-shaped Al substrate with NiCrOx and SnO2 conformal coatings. We must in this case maximize the solar absorption α while minimizing the thermal emissivity ɛ in the infrared. A multi-objective genetic algorithm has to be implemented in this case in order to determine optimal geometrical parameters. The parameters we obtained using the multi-objective GA enable α~97.8% and ɛ~4.8%, which improves results achieved previously when considering a flat substrate. These two applications demonstrate the interest of genetic algorithms for addressing complex problems in physics.

Optimization of HAART with genetic algorithms and agent-based models of HIV infection.

PubMed

Castiglione, F; Pappalardo, F; Bernaschi, M; Motta, S

2007-12-15

Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection. The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups. A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html

Therapeutic application of RNAi: is mRNA targeting finally ready for prime time?

PubMed Central

Grimm, Dirk; Kay, Mark A.

2007-01-01

With unprecedented speed, RNA interference (RNAi) has advanced from its basic discovery in lower organisms to becoming a powerful genetic tool and perhaps our single most promising biotherapeutic for a wide array of diseases. Numerous studies document RNAi efficacy in laboratory animals, and the first clinical trials are underway and thus far suggest that RNAi is safe to use in humans. Yet substantial hurdles have also surfaced and must be surmounted before therapeutic RNAi applications can become a standard therapy. Here we review the most critical roadblocks and concerns for clinical RNAi transition, delivery, and safety. We highlight emerging solutions and concurrently discuss novel therapeutic RNAi-based concepts. The current rapid advances create realistic optimism that the establishment of RNAi as a new and potent clinical modality in humans is near. PMID:18060021

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1990-01-01

A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine using the principle of increasing precision with decreasing intelligence from a hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution can be found by minimizing the total entropy in the system. The focus is on the design of the organization level as a Boltzmann machine. Since this level is responsible for planning the actions of the machine, the Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence and illustrated with numerical examples.

Phylogenetic Quantification of Intra-tumour Heterogeneity

PubMed Central

Schwarz, Roland F.; Trinh, Anne; Sipos, Botond; Brenton, James D.; Goldman, Nick; Markowetz, Florian

2014-01-01

Intra-tumour genetic heterogeneity is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance, and if so, would have prognostic and predictive utility. However, methods for objectively quantifying tumour heterogeneity have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements. To address these challenges, we present MEDICC, a method for phylogenetic reconstruction and heterogeneity quantification based on a Minimum Event Distance for Intra-tumour Copy-number Comparisons. Using a transducer-based pairwise comparison function, we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy, and additionally allows unbiased numerical quantification of tumour heterogeneity. Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data. PMID:24743184

Optimising the production of succinate and lactate in Escherichia coli using a hybrid of artificial bee colony algorithm and minimisation of metabolic adjustment.

PubMed

Tang, Phooi Wah; Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Napis, Suhaimi

2015-03-01

Metabolic engineering is a research field that focuses on the design of models for metabolism, and uses computational procedures to suggest genetic manipulation. It aims to improve the yield of particular chemical or biochemical products. Several traditional metabolic engineering methods are commonly used to increase the production of a desired target, but the products are always far below their theoretical maximums. Using numeral optimisation algorithms to identify gene knockouts may stall at a local minimum in a multivariable function. This paper proposes a hybrid of the artificial bee colony (ABC) algorithm and the minimisation of metabolic adjustment (MOMA) to predict an optimal set of solutions in order to optimise the production rate of succinate and lactate. The dataset used in this work was from the iJO1366 Escherichia coli metabolic network. The experimental results include the production rate, growth rate and a list of knockout genes. From the comparative analysis, ABCMOMA produced better results compared to previous works, showing potential for solving genetic engineering problems. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Expanding a dynamic flux balance model of yeast fermentation to genome-scale

PubMed Central

2011-01-01

Background Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints. Results Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol. Conclusion A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations. PMID:21595919

Engineering host-derived resistance against plant parasites through RNA interference: challenges and opportunities.

PubMed

Runo, Steven

2011-01-01

RNA interference (RNAi) has rapidly advanced to become a powerful genetic tool and holds promise to revolutionizing agriculture by providing a strategy for controlling a wide array of crop pests. Numerous studies document RNAi efficacy in achieving silencing in viruses, insects, nematodes and weeds parasitizing crops. In general, host derived pest resistance through RNAi is achieved by genetically transforming host plants with double stranded RNA constructs targeted at essential parasite genes leading to generation of small interfering RNAs (siRNAs). Small interfering RNAs formed in the host are then delivered to the parasite and transported to target cells. Delivery can be oral - worms and insects, viral infections, viruses - or through a vascular connections - parasitic plants, while delivery to target cells is by cell to cell systemic movement of the silencing signal. Despite the overall optimism in generating pest resistant crops through RNAi-mediated silencing, some hurdles have recently begun to emerge. Presently, the main challenge is delivery of sufficient siRNAs, in the right cells, and at the right time to mount; a strong, durable, and broad-spectrum posttranscriptional gene silencing (PTGS) signal. This review highlights the novel strategies available for improving host derived RNAi resistance in downstream applied agriculture.

Multi channel thermal hydraulic analysis of gas cooled fast reactor using genetic algorithm

NASA Astrophysics Data System (ADS)

Drajat, R. Z.; Su'ud, Z.; Soewono, E.; Gunawan, A. Y.

2012-05-01

There are three analyzes to be done in the design process of nuclear reactor i.e. neutronic analysis, thermal hydraulic analysis and thermodynamic analysis. The focus in this article is the thermal hydraulic analysis, which has a very important role in terms of system efficiency and the selection of the optimal design. This analysis is performed in a type of Gas Cooled Fast Reactor (GFR) using cooling Helium (He). The heat from nuclear fission reactions in nuclear reactors will be distributed through the process of conduction in fuel elements. Furthermore, the heat is delivered through a process of heat convection in the fluid flow in cooling channel. Temperature changes that occur in the coolant channels cause a decrease in pressure at the top of the reactor core. The governing equations in each channel consist of mass balance, momentum balance, energy balance, mass conservation and ideal gas equation. The problem is reduced to finding flow rates in each channel such that the pressure drops at the top of the reactor core are all equal. The problem is solved numerically with the genetic algorithm method. Flow rates and temperature distribution in each channel are obtained here.

An Introduction to Computational Physics

NASA Astrophysics Data System (ADS)

Pang, Tao

2010-07-01

Preface to first edition; Preface; Acknowledgements; 1. Introduction; 2. Approximation of a function; 3. Numerical calculus; 4. Ordinary differential equations; 5. Numerical methods for matrices; 6. Spectral analysis; 7. Partial differential equations; 8. Molecular dynamics simulations; 9. Modeling continuous systems; 10. Monte Carlo simulations; 11. Genetic algorithm and programming; 12. Numerical renormalization; References; Index.

An Improved Transformation and Optimized Sampling Scheme for the Numerical Evaluation of Singular and Near-Singular Potentials

NASA Technical Reports Server (NTRS)

Khayat, Michael A.; Wilton, Donald R.; Fink, Patrick W.

2007-01-01

Simple and efficient numerical procedures using singularity cancellation methods are presented for evaluating singular and near-singular potential integrals. Four different transformations are compared and the advantages of the Radial-angular transform are demonstrated. A method is then described for optimizing this integration scheme.

The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model

NASA Astrophysics Data System (ADS)

Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

2016-05-01

Dynamic immunizations, under which the state of the propagation network of electronic viruses can be changed by adjusting the control measures, are regarded as an alternative to static immunizations. This paper addresses the optimal dynamical immunization under the widely accepted SIRS assumption. First, based on a controlled heterogeneous node-based SIRS model, an optimal control problem capturing the optimal dynamical immunization is formulated. Second, the existence of an optimal dynamical immunization scheme is shown, and the corresponding optimality system is derived. Next, some numerical examples are given to show that an optimal immunization strategy can be worked out by numerically solving the optimality system, from which it is found that the network topology has a complex impact on the optimal immunization strategy. Finally, the difference between a payoff and the minimum payoff is estimated in terms of the deviation of the corresponding immunization strategy from the optimal immunization strategy. The proposed optimal immunization scheme is justified, because it can achieve a low level of infections at a low cost.

Warpage improvement on wheel caster by optimizing the process parameters using genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, the defects will always encountered and affected the final product shape and functionality. This study is concerning on minimizing warpage and optimizing the process parameter of injection moulding part. Apart from eliminating product wastes, this project also giving out best recommended parameters setting. This research studied on five parameters. The optimization showed that warpage have been improved 42.64% from 0.6524 mm to 0.30879 mm in Autodesk Moldflow Insight (AMI) simulation result and Genetic Algorithm (GA) respectively.

Optimization of algorithm of coding of genetic information of Chlamydia

NASA Astrophysics Data System (ADS)

Feodorova, Valentina A.; Ulyanov, Sergey S.; Zaytsev, Sergey S.; Saltykov, Yury V.; Ulianova, Onega V.

2018-04-01

New method of coding of genetic information using coherent optical fields is developed. Universal technique of transformation of nucleotide sequences of bacterial gene into laser speckle pattern is suggested. Reference speckle patterns of the nucleotide sequences of omp1 gene of typical wild strains of Chlamydia trachomatis of genovars D, E, F, G, J and K and Chlamydia psittaci serovar I as well are generated. Algorithm of coding of gene information into speckle pattern is optimized. Fully developed speckles with Gaussian statistics for gene-based speckles have been used as criterion of optimization.

A method to optimize the shield compact and lightweight combining the structure with components together by genetic algorithm and MCNP code.

PubMed

Cai, Yao; Hu, Huasi; Pan, Ziheng; Hu, Guang; Zhang, Tao

2018-05-17

To optimize the shield for neutrons and gamma rays compact and lightweight, a method combining the structure and components together was established employing genetic algorithms and MCNP code. As a typical case, the fission energy spectrum of 235 U which mixed neutrons and gamma rays was adopted in this study. Six types of materials were presented and optimized by the method. Spherical geometry was adopted in the optimization after checking the geometry effect. Simulations have made to verify the reliability of the optimization method and the efficiency of the optimized materials. To compare the materials visually and conveniently, the volume and weight needed to build a shield are employed. The results showed that, the composite multilayer material has the best performance. Copyright © 2018 Elsevier Ltd. All rights reserved.

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

Laplace-Fourier-domain dispersion analysis of an average derivative optimal scheme for scalar-wave equation

NASA Astrophysics Data System (ADS)

Chen, Jing-Bo

2014-06-01

By using low-frequency components of the damped wavefield, Laplace-Fourier-domain full waveform inversion (FWI) can recover a long-wavelength velocity model from the original undamped seismic data lacking low-frequency information. Laplace-Fourier-domain modelling is an important foundation of Laplace-Fourier-domain FWI. Based on the numerical phase velocity and the numerical attenuation propagation velocity, a method for performing Laplace-Fourier-domain numerical dispersion analysis is developed in this paper. This method is applied to an average-derivative optimal scheme. The results show that within the relative error of 1 per cent, the Laplace-Fourier-domain average-derivative optimal scheme requires seven gridpoints per smallest wavelength and smallest pseudo-wavelength for both equal and unequal directional sampling intervals. In contrast, the classical five-point scheme requires 23 gridpoints per smallest wavelength and smallest pseudo-wavelength to achieve the same accuracy. Numerical experiments demonstrate the theoretical analysis.

Advanced rotorcraft control using parameter optimization

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters is presented. The algorithm is part of a design algorithm for an optimal linear dynamic output feedback controller that minimizes a finite time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed loop eigensystem. This approach through the use of a accurate Pade series approximation does not require the closed loop system matrix to be diagonalizable. The algorithm has been included in a control design package for optimal robust low order controllers. Usefulness of the proposed numerical algorithm has been demonstrated using numerous practical design cases where degeneracies occur frequently in the closed loop system under an arbitrary controller design initialization and during the numerical search.

A genetic algorithm approach in interface and surface structure optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jian

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less

A method for the dynamic management of genetic variability in dairy cattle

PubMed Central

Colleau, Jean-Jacques; Moureaux, Sophie; Briend, Michèle; Bechu, Jérôme

2004-01-01

According to the general approach developed in this paper, dynamic management of genetic variability in selected populations of dairy cattle is carried out for three simultaneous purposes: procreation of young bulls to be further progeny-tested, use of service bulls already selected and approval of recently progeny-tested bulls for use. At each step, the objective is to minimize the average pairwise relationship coefficient in the future population born from programmed matings and the existing population. As a common constraint, the average estimated breeding value of the new population, for a selection goal including many important traits, is set to a desired value. For the procreation of young bulls, breeding costs are additionally constrained. Optimization is fully analytical and directly considers matings. Corresponding algorithms are presented in detail. The efficiency of these procedures was tested on the current Norman population. Comparisons between optimized and real matings, clearly showed that optimization would have saved substantial genetic variability without reducing short-term genetic gains. PMID:15231230

An introduction to the COLIN optimization interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, William Eugene

2003-03-01

We describe COLIN, a Common Optimization Library INterface for C++. COLIN provides C++ template classes that define a generic interface for both optimization problems and optimization solvers. COLIN is specifically designed to facilitate the development of hybrid optimizers, for which one optimizer calls another to solve an optimization subproblem. We illustrate the capabilities of COLIN with an example of a memetic genetic programming solver.

Simulation of the detonation process of an ammonium nitrate based emulsion explosive using the Lee-Tarver reactive flow model

NASA Astrophysics Data System (ADS)

Ribeiro, Jose; Silva, Cristovao; Mendes, Ricardo; Plaksin, Igor; Campos, Jose

2011-06-01

The use of emulsion explosives [EEx] for processing materials (compaction, welding and forming) requires the ability to perform detailed simulations of its detonation process [DP]. Detailed numerical simulations of the DP of this kind of explosives, characterized by having a finite reaction zone thickness, are thought to be suitable performed using the Lee-Tarver reactive flow model. In this work a real coded genetic algorithm methodology was used to estimate the 15 parameters of the reaction rate equation [RRE] of that model for a particular EEx. This methodology allows, in a single optimization procedure, using only one experimental result and without the need of any starting solution, to seek for the 15 parameters of the RRE that fit the numerical to the experimental results. Mass averaging and the Plate-Gap Model have been used for the determination of the shock data used in the unreacted explosive JWL EoS assessment and the thermochemical code THOR retrieved the data used in the detonation products JWL EoS assessment. The obtained parameters allow a good description of the experimental data and show some peculiarities arising from the intrinsic nature of this kind of composite explosive.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

USDA-ARS?s Scientific Manuscript database

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

Overview: Applications of numerical optimization methods to helicopter design problems

NASA Technical Reports Server (NTRS)

Miura, H.

1984-01-01

There are a number of helicopter design problems that are well suited to applications of numerical design optimization techniques. Adequate implementation of this technology will provide high pay-offs. There are a number of numerical optimization programs available, and there are many excellent response/performance analysis programs developed or being developed. But integration of these programs in a form that is usable in the design phase should be recognized as important. It is also necessary to attract the attention of engineers engaged in the development of analysis capabilities and to make them aware that analysis capabilities are much more powerful if integrated into design oriented codes. Frequently, the shortcoming of analysis capabilities are revealed by coupling them with an optimization code. Most of the published work has addressed problems in preliminary system design, rotor system/blade design or airframe design. Very few published results were found in acoustics, aerodynamics and control system design. Currently major efforts are focused on vibration reduction, and aerodynamics/acoustics applications appear to be growing fast. The development of a computer program system to integrate the multiple disciplines required in helicopter design with numerical optimization technique is needed. Activities in Britain, Germany and Poland are identified, but no published results from France, Italy, the USSR or Japan were found.

Neighboring extremal optimal control design including model mismatch errors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, T.J.; Hull, D.G.

1994-11-01

The mismatch control technique that is used to simplify model equations of motion in order to determine analytic optimal control laws is extended using neighboring extremal theory. The first variation optimal control equations are linearized about the extremal path to account for perturbations in the initial state and the final constraint manifold. A numerical example demonstrates that the tuning procedure inherent in the mismatch control method increases the performance of the controls to the level of a numerically-determined piecewise-linear controller.

Numerical study of a matrix-free trust-region SQP method for equality constrained optimization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heinkenschloss, Matthias; Ridzal, Denis; Aguilo, Miguel Antonio

2011-12-01

This is a companion publication to the paper 'A Matrix-Free Trust-Region SQP Algorithm for Equality Constrained Optimization' [11]. In [11], we develop and analyze a trust-region sequential quadratic programming (SQP) method that supports the matrix-free (iterative, in-exact) solution of linear systems. In this report, we document the numerical behavior of the algorithm applied to a variety of equality constrained optimization problems, with constraints given by partial differential equations (PDEs).

Numerical and experimental analysis of a ducted propeller designed by a fully automated optimization process under open water condition

NASA Astrophysics Data System (ADS)

Yu, Long; Druckenbrod, Markus; Greve, Martin; Wang, Ke-qi; Abdel-Maksoud, Moustafa

2015-10-01

A fully automated optimization process is provided for the design of ducted propellers under open water conditions, including 3D geometry modeling, meshing, optimization algorithm and CFD analysis techniques. The developed process allows the direct integration of a RANSE solver in the design stage. A practical ducted propeller design case study is carried out for validation. Numerical simulations and open water tests are fulfilled and proved that the optimum ducted propeller improves hydrodynamic performance as predicted.

Development of Pelton turbine using numerical simulation

NASA Astrophysics Data System (ADS)

Patel, K.; Patel, B.; Yadav, M.; Foggia, T.

2010-08-01

This paper describes recent research and development activities in the field of Pelton turbine design. Flow inside Pelton turbine is most complex due to multiphase (mixture of air and water) and free surface in nature. Numerical calculation is useful to understand flow physics as well as effect of geometry on flow. The optimized design is obtained using in-house special optimization loop. Either single phase or two phase unsteady numerical calculation could be performed. Numerical results are used to visualize the flow pattern in the water passage and to predict performance of Pelton turbine at full load as well as at part load. Model tests are conducted to determine performance of turbine and it shows good agreement with numerically predicted performance.

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Latent spatial models and sampling design for landscape genetics

Treesearch

Ephraim M. Hanks; Melvin B. Hooten; Steven T. Knick; Sara J. Oyler-McCance; Jennifer A. Fike; Todd B. Cross; Michael K. Schwartz

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial...

Fast Numerical Methods for the Design of Layered Photonic Structures with Rough Interfaces

NASA Technical Reports Server (NTRS)

Komarevskiy, Nikolay; Braginsky, Leonid; Shklover, Valery; Hafner, Christian; Lawson, John

2011-01-01

Modified boundary conditions (MBC) and a multilayer approach (MA) are proposed as fast and efficient numerical methods for the design of 1D photonic structures with rough interfaces. These methods are applicable for the structures, composed of materials with arbitrary permittivity tensor. MBC and MA are numerically validated on different types of interface roughness and permittivities of the constituent materials. The proposed methods can be combined with the 4x4 scattering matrix method as a field solver and an evolutionary strategy as an optimizer. The resulted optimization procedure is fast, accurate, numerically stable and can be used to design structures for various applications.

Salmonella enterica serovar Typhimurium utilizes the ClpPX and Lon proteases for optimal fitness in the ceca of chickens.

PubMed

Troxell, Bryan

2016-07-01

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a leading cause of salmonellosis. Poultry and poultry products are implicated in transmission of Salmonella to humans. In 2013, an outbreak of S Typhimurium occurred that comprised 39 states within the United States and was associated with backyard flocks of chickens. Colonization of the avian host by S Typhimurium requires numerous genetic factors encoded within the bacterium. Of particular interest are genetic factors induced by alternative sigma factors within S Typhimurium since these genetic elements are important for adaptation to different environmental stresses. The heat shock response is a dedicated change in gene regulation within bacteria in response to several stresses, specifically growth at 42°C. Because chickens have a higher body temperature than other animals (42°C) the hypothesis was tested that components of the heat shock response are important for optimal fitness within the chicken. To this end, deletion of the heat shock proteases clpPX (BTNC0022) or lon (BTNC0021) was accomplished and the bacterial fitness in vivo was compared to the "wild-type" strain (NC1040) using a competition assay. One-day-old chicks were orally gavaged with an equal mixture of NC1040 and either BTNC0022 or BTNC0021. Quantification of viable bacteria over time by using plate counts indicated that deletion of either heat shock protease resulted in significantly reduced colonization of the chicken ceca compared to the wild-type strain. To satisfy the molecular Koch's postulates, clpPX and lon mutants were complemented in trans using a low-copy number plasmid for additional in vivo experiments. Complementation studies confirmed the importance of either heat shock protease to colonization of the chicken ceca. This report demonstrated that both ClpPX and Lon were important for optimal fitness within chickens. Moreover, these results suggested that components of the heat shock may be critical factors used by S. Typhimurium for colonization of poultry. The use of feed additives or other treatments that inactivate or inhibit ClpPX or Lon may reduce the bacterial burden of S. Typhimurium in poultry. © The Author 2016. Published by Oxford University Press on behalf of Poultry Science Association.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Recourse-based facility-location problems in hybrid uncertain environment.

PubMed

Wang, Shuming; Watada, Junzo; Pedrycz, Witold

2010-08-01

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

2014-03-15

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented tomore » tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.« less

The influence of dispositional optimism on post-visit anxiety and risk perception accuracy among breast cancer genetic counselees.

PubMed

Wiering, B M; Albada, A; Bensing, J M; Ausems, M G E M; van Dulmen, A M

2013-11-01

Much is unknown about the influence of dispositional optimism and affective communication on genetic counselling outcomes. This study investigated the influence of counselees' optimism on the counselees' risk perception accuracy and anxiety, while taking into account the affective communication during the first consultation for breast cancer genetic counselling. Counselees completed questionnaires measuring optimism, anxiety and the perceived risk that hereditary breast cancer runs in the family before, and anxiety and perceived risk after the first consultation. Consultations were videotaped. The duration of eye contact was measured, and verbal communication was rated using the Roter Interaction Analysis System. Less-optimistic counselees were more anxious post-visit (β = -.29; p = .00). Counsellors uttered fewer reassuring statements if counselees were more anxious (β = -.84; p = .00) but uttered more reassurance if counselees were less optimistic (β = -.76; p = .01). Counsellors expressed less empathy if counselees perceived their risk as high (β = -1.51; p = .04). An increase in the expression of reassurance was related to less post-visit anxiety (β = -.35; p = .03). More empathy was related to a greater overestimation of risk (β = .92; p = .01). Identification of a lack of optimism as a risk factor for high anxiety levels enables the adaptation of affective communication to improve genetic counselling outcomes. Because reassurance was related to less anxiety, beneficial adaptation is attainable by increasing counsellors' reassurance, if possible. Because of a lack of optimally adapted communication in this study, further research is needed to clarify how to increase counsellors' ability to adapt to counselees. Copyright © 2013 John Wiley & Sons, Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Teaching and Learning Numerical Analysis and Optimization: A Didactic Framework and Applications of Inquiry-Based Learning

ERIC Educational Resources Information Center

Lappas, Pantelis Z.; Kritikos, Manolis N.

2018-01-01

The main objective of this paper is to propose a didactic framework for teaching Applied Mathematics in higher education. After describing the structure of the framework, several applications of inquiry-based learning in teaching numerical analysis and optimization are provided to illustrate the potential of the proposed framework. The framework…

Numerous clones resistant to Phytophthora palmivora in the "Guiana" genetic group of Theobroma cacao L.

PubMed

Thevenin, Jean-Marc; Rossi, Vivien; Ducamp, Michel; Doare, Fabien; Condina, Virgile; Lachenaud, Philippe

2012-01-01

Cocoa black pod rot, a disease caused by Stramenopiles of the genus Phytophthora, and particularly by the pan-tropical species P. palmivora, causes serious production losses worldwide. In order to reduce the impact of these pests and diseases, preference is given to genetic control using resistant varieties and, to that end, breeders seek sources of resistance in wild cocoa trees. For instance, surveys of spontaneous cocoa trees in French Guiana between 1985 and 1995 led to the collection of abundant plant material forming a particular genetic group (the "Guiana" group). Following numerous one-off studies demonstrating the merits of this group as a source of resistance to Phytophthora, this article presents the results of a comprehensive study assessing the resistance of 186 "Guiana" clones in relation to the Guianan strain (GY 27) of P. palmivora. This study, undertaken in French Guiana, using an efficient methodology (ten series of tests and a statistical test adapted to the ordinal nature of the data) confirmed that the "Guiana" genetic group does indeed constitute an important source of resistance to P. palmivora, though with some variations depending on the demes of origin. Numerous clones (59) proved to be as resistant as the SCAVINA 6 resistance control, whilst nine were statistically more resistant. The "Resistant" and "Moderately Resistant" Guianan clones totalled 108 (58% of the total tested). Some of the clones more resistant than SCAVINA 6 could be incorporated into numerous cocoa breeding programmes, particularly those that also display other notable qualities. The same applies for numerous other clones equivalent to SCAVINA 6, especially the "elite"' clones GU 134-B, GU 139-A and GU 285-A.

designGG: an R-package and web tool for the optimal design of genetical genomics experiments.

PubMed

Li, Yang; Swertz, Morris A; Vera, Gonzalo; Fu, Jingyuan; Breitling, Rainer; Jansen, Ritsert C

2009-06-18

High-dimensional biomolecular profiling of genetically different individuals in one or more environmental conditions is an increasingly popular strategy for exploring the functioning of complex biological systems. The optimal design of such genetical genomics experiments in a cost-efficient and effective way is not trivial. This paper presents designGG, an R package for designing optimal genetical genomics experiments. A web implementation for designGG is available at http://gbic.biol.rug.nl/designGG. All software, including source code and documentation, is freely available. DesignGG allows users to intelligently select and allocate individuals to experimental units and conditions such as drug treatment. The user can maximize the power and resolution of detecting genetic, environmental and interaction effects in a genome-wide or local mode by giving more weight to genome regions of special interest, such as previously detected phenotypic quantitative trait loci. This will help to achieve high power and more accurate estimates of the effects of interesting factors, and thus yield a more reliable biological interpretation of data. DesignGG is applicable to linkage analysis of experimental crosses, e.g. recombinant inbred lines, as well as to association analysis of natural populations.

An Introduction to Computational Physics - 2nd Edition

NASA Astrophysics Data System (ADS)

Pang, Tao

2006-01-01

Preface to first edition; Preface; Acknowledgements; 1. Introduction; 2. Approximation of a function; 3. Numerical calculus; 4. Ordinary differential equations; 5. Numerical methods for matrices; 6. Spectral analysis; 7. Partial differential equations; 8. Molecular dynamics simulations; 9. Modeling continuous systems; 10. Monte Carlo simulations; 11. Genetic algorithm and programming; 12. Numerical renormalization; References; Index.

Simulation and Optimization of Large Scale Subsurface Environmental Impacts; Investigations, Remedial Design and Long Term Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deschaine, L.M.; Chalmers Univ. of Technology, Dept. of Physical Resources, Complex Systems Group, Goteborg

2008-07-01

The global impact to human health and the environment from large scale chemical / radionuclide releases is well documented. Examples are the wide spread release of radionuclides from the Chernobyl nuclear reactors, the mobilization of arsenic in Bangladesh, the formation of Environmental Protection Agencies in the United States, Canada and Europe, and the like. The fiscal costs of addressing and remediating these issues on a global scale are astronomical, but then so are the fiscal and human health costs of ignoring them. An integrated methodology for optimizing the response(s) to these issues is needed. This work addresses development of optimalmore » policy design for large scale, complex, environmental issues. It discusses the development, capabilities, and application of a hybrid system of algorithms that optimizes the environmental response. It is important to note that 'optimization' does not singularly refer to cost minimization, but to the effective and efficient balance of cost, performance, risk, management, and societal priorities along with uncertainty analysis. This tool integrates all of these elements into a single decision framework. It provides a consistent approach to designing optimal solutions that are tractable, traceable, and defensible. The system is modular and scalable. It can be applied either as individual components or in total. By developing the approach in a complex systems framework, a solution methodology represents a significant improvement over the non-optimal 'trial and error' approach to environmental response(s). Subsurface environmental processes are represented by linear and non-linear, elliptic and parabolic equations. The state equations solved using numerical methods include multi-phase flow (water, soil gas, NAPL), and multicomponent transport (radionuclides, heavy metals, volatile organics, explosives, etc.). Genetic programming is used to generate the simulators either when simulation models do not exist, or to extend the accuracy of them. The uncertainty and sparse nature of information in earth science simulations necessitate stochastic representations. For discussion purposes, the solution to these site-wide challenges is divided into three sub-components; plume finding, long term monitoring, and site-wide remediation. Plume finding is the optimal estimation of the plume fringe(s) at a specified time. It is optimized by fusing geo-stochastic flow and transport simulations with the information content of data using a Kalman filter. The result is an optimal monitoring sensor network; the decision variable is location(s) of sensor in three dimensions. Long term monitoring extends this approach concept, and integrates the spatial-time correlations to optimize the decision variables of where to sample and when to sample over the project life cycle. Optimization of location and timing of samples to meet the desired accuracy of temporal plume movement is accomplished using enumeration or genetic algorithms. The remediation optimization solves the multi-component, multiphase system of equations and incorporates constraints on life-cycle costs, maximum annual costs, maximum allowable annual discharge (for assessing the monitored natural attenuation solution) and constraints on where remedial system component(s) can be located, including management overrides to force certain solutions to be chosen are incorporated for solution design. It uses a suite of optimization techniques, including the outer approximation method, Lipchitz global optimization, genetic algorithms, and the like. The automated optimal remedial design algorithm requires a stable simulator be available for the simulated process. This is commonly the case for all above specifications sans true three-dimensional multiphase flow. Much work is currently being conducted in the industry to develop stable 3D, three-phase simulators. If needed, an interim heuristic algorithm is available to get close to optimal for these conditions. This system process provides the full capability to optimize multi-source, multiphase, and multicomponent sites. The results of applying just components of these algorithms have produced predicted savings of as much as $90,000,000(US), when compared to alternative solutions. Investment in a pilot program to test the model saved 100% of the $20,000,000 predicted for the smaller test implementation. This was done without loss of effectiveness, and received an award from the Vice President - and now Nobel peace prize winner - Al Gore of the United States. (authors)« less

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Genetic barcodes

DOEpatents

Weier, Heinz -Ulrich G

2015-08-04

Herein are described multicolor FISH probe sets termed "genetic barcodes" targeting several cancer or disease-related loci to assess gene rearrangements and copy number changes in tumor cells. Two, three or more different fluorophores are used to detect the genetic barcode sections thus permitting unique labeling and multilocus analysis in individual cell nuclei. Gene specific barcodes can be generated and combined to provide both numerical and structural genetic information for these and other pertinent disease associated genes.

Neural-network-assisted genetic algorithm applied to silicon clusters

NASA Astrophysics Data System (ADS)

Marim, L. R.; Lemes, M. R.; dal Pino, A.

2003-03-01

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Finite burn maneuver modeling for a generalized spacecraft trajectory design and optimization system.

PubMed

Ocampo, Cesar

2004-05-01

The modeling, design, and optimization of finite burn maneuvers for a generalized trajectory design and optimization system is presented. A generalized trajectory design and optimization system is a system that uses a single unified framework that facilitates the modeling and optimization of complex spacecraft trajectories that may operate in complex gravitational force fields, use multiple propulsion systems, and involve multiple spacecraft. The modeling and optimization issues associated with the use of controlled engine burn maneuvers of finite thrust magnitude and duration are presented in the context of designing and optimizing a wide class of finite thrust trajectories. Optimal control theory is used examine the optimization of these maneuvers in arbitrary force fields that are generally position, velocity, mass, and are time dependent. The associated numerical methods used to obtain these solutions involve either, the solution to a system of nonlinear equations, an explicit parameter optimization method, or a hybrid parameter optimization that combines certain aspects of both. The theoretical and numerical methods presented here have been implemented in copernicus, a prototype trajectory design and optimization system under development at the University of Texas at Austin.

Integrative systems modeling and multi-objective optimization

EPA Science Inventory

This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

Ortho Image and DTM Generation with Intelligent Methods

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadeghian, S.

2013-10-01

Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

Optimal power flow with optimal placement TCSC device on 500 kV Java-Bali electrical power system using genetic Algorithm-Taguchi method

NASA Astrophysics Data System (ADS)

Apribowo, Chico Hermanu Brillianto; Ibrahim, Muhammad Hamka; Wicaksono, F. X. Rian

2018-02-01

The growing burden of the load and the complexity of the power system has had an impact on the need for optimization of power system operation. Optimal power flow (OPF) with optimal location placement and rating of thyristor controlled series capacitor (TCSC) is an effective solution used to determine the economic cost of operating the plant and regulate the power flow in the power system. The purpose of this study is to minimize the total cost of generation by placing the location and the optimal rating of TCSC using genetic algorithm-design of experiment techniques (GA-DOE). Simulation on Java-Bali system 500 kV with the amount of TCSC used by 5 compensator, the proposed method can reduce the generation cost by 0.89% compared to OPF without using TCSC.

Intelligent design optimization of a shape-memory-alloy-actuated reconfigurable wing

NASA Astrophysics Data System (ADS)

Lagoudas, Dimitris C.; Strelec, Justin K.; Yen, John; Khan, Mohammad A.

2000-06-01

The unique thermal and mechanical properties offered by shape memory alloys (SMAs) present exciting possibilities in the field of aerospace engineering. When properly trained, SMA wires act as linear actuators by contracting when heated and returning to their original shape when cooled. It has been shown experimentally that the overall shape of an airfoil can be altered by activating several attached SMA wire actuators. This shape-change can effectively increase the efficiency of a wing in flight at several different flow regimes. To determine the necessary placement of these wire actuators within the wing, an optimization method that incorporates a fully-coupled structural, thermal, and aerodynamic analysis has been utilized. Due to the complexity of the fully-coupled analysis, intelligent optimization methods such as genetic algorithms have been used to efficiently converge to an optimal solution. The genetic algorithm used in this case is a hybrid version with global search and optimization capabilities augmented by the simplex method as a local search technique. For the reconfigurable wing, each chromosome represents a realizable airfoil configuration and its genes are the SMA actuators, described by their location and maximum transformation strain. The genetic algorithm has been used to optimize this design problem to maximize the lift-to-drag ratio for a reconfigured airfoil shape.

Achieving definitive results in long-chain polyunsaturated fatty acid supplementation trials of term infants: factors for consideration.

PubMed

Meldrum, Suzanne J; Smith, Michael A; Prescott, Susan L; Hird, Kathryn; Simmer, Karen

2011-04-01

Numerous randomized controlled trials (RCTs) have been undertaken to determine whether supplementation with long-chain polyunsaturated fatty acids (LCPUFAs) in infancy would improve the developmental outcomes of term infants. The results of such trials have been thoroughly reviewed with no definitive conclusion as to the efficacy of LCPUFA supplementation. A number of reasons for the lack of conclusive findings in this area have been proposed. This review examines such factors with the aim of determining whether an optimal method of investigation for RCTs of LCPUFA supplementation in term infants can be ascertained from previous research. While more research is required to completely inform a method that is likely to achieve definitive results, the findings of this literature review indicate future trials should investigate the effects of sex, genetic polymorphisms, the specific effects of LCPUFAs, and the optimal tests for neurodevelopmental assessment. The current literature indicates a docosahexaenoic acid dose of 0.32%, supplementation from birth to 12 months, and a total sample size of at least 286 (143 per group) should be included in the methodology of future trials. © 2011 International Life Sciences Institute.

Design and optimization of membrane-type acoustic metamaterials

NASA Astrophysics Data System (ADS)

Blevins, Matthew Grant

One of the most common problems in noise control is the attenuation of low frequency noise. Typical solutions require barriers with high density and/or thickness. Membrane-type acoustic metamaterials are a novel type of engineered material capable of high low-frequency transmission loss despite their small thickness and light weight. These materials are ideally suited to applications with strict size and weight limitations such as aircraft, automobiles, and buildings. The transmission loss profile can be manipulated by changing the micro-level substructure, stacking multiple unit cells, or by creating multi-celled arrays. To date, analysis has focused primarily on experimental studies in plane-wave tubes and numerical modeling using finite element methods. These methods are inefficient when used for applications that require iterative changes to the structure of the material. To facilitate design and optimization of membrane-type acoustic metamaterials, computationally efficient dynamic models based on the impedance-mobility approach are proposed. Models of a single unit cell in a waveguide and in a baffle, a double layer of unit cells in a waveguide, and an array of unit cells in a baffle are studied. The accuracy of the models and the validity of assumptions used are verified using a finite element method. The remarkable computational efficiency of the impedance-mobility models compared to finite element methods enables implementation in design tools based on a graphical user interface and in optimization schemes. Genetic algorithms are used to optimize the unit cell design for a variety of noise reduction goals, including maximizing transmission loss for broadband, narrow-band, and tonal noise sources. The tools for design and optimization created in this work will enable rapid implementation of membrane-type acoustic metamaterials to solve real-world noise control problems.

Robust Flight Path Determination for Mars Precision Landing Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Bayard, David S.; Kohen, Hamid

1997-01-01

This paper documents the application of genetic algorithms (GAs) to the problem of robust flight path determination for Mars precision landing. The robust flight path problem is defined here as the determination of the flight path which delivers a low-lift open-loop controlled vehicle to its desired final landing location while minimizing the effect of perturbations due to uncertainty in the atmospheric model and entry conditions. The genetic algorithm was capable of finding solutions which reduced the landing error from 111 km RMS radial (open-loop optimal) to 43 km RMS radial (optimized with respect to perturbations) using 200 hours of computation on an Ultra-SPARC workstation. Further reduction in the landing error is possible by going to closed-loop control which can utilize the GA optimized paths as nominal trajectories for linearization.

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

PubMed Central

Norouzi, Ali; Zaim, A. Halim

2014-01-01

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

Numerical optimization of perturbative coils for tokamaks

NASA Astrophysics Data System (ADS)

Lazerson, Samuel; Park, Jong-Kyu; Logan, Nikolas; Boozer, Allen; NSTX-U Research Team

2014-10-01

Numerical optimization of coils which apply three dimensional (3D) perturbative fields to tokamaks is presented. The application of perturbative 3D magnetic fields in tokamaks is now commonplace for control of error fields, resistive wall modes, resonant field drive, and neoclassical toroidal viscosity (NTV) torques. The design of such systems has focused on control of toroidal mode number, with coil shapes based on simple window-pane designs. In this work, a numerical optimization suite based on the STELLOPT 3D equilibrium optimization code is presented. The new code, IPECOPT, replaces the VMEC equilibrium code with the IPEC perturbed equilibrium code, and targets NTV torque by coupling to the PENT code. Fixed boundary optimizations of the 3D fields for the NSTX-U experiment are underway. Initial results suggest NTV torques can be driven by normal field spectrums which are not pitch-resonant with the magnetic field lines. Work has focused on driving core torque with n = 1 and edge torques with n = 3 fields. Optimizations of the coil currents for the planned NSTX-U NCC coils highlight the code's free boundary capability. This manuscript has been authored by Princeton University under Contract Number DE-AC02-09CH11466 with the U.S. Department of Energy.

Study of motion of optimal bodies in the soil of grid method

NASA Astrophysics Data System (ADS)

Kotov, V. L.; Linnik, E. Yu

2016-11-01

The paper presents a method of calculating the optimum forms in axisymmetric numerical method based on the Godunov and models elastoplastic soil vedium Grigoryan. Solved two problems in a certain definition of generetrix rotation of the body of a given length and radius of the base, having a minimum impedance and maximum penetration depth. Numerical calculations are carried out by a modified method of local variations, which allows to significantly reduce the number of operations at different representations of generetrix. Significantly simplify the process of searching for optimal body allows the use of a quadratic model of local interaction for preliminary assessments. It is noted the qualitative similarity of the process of convergence of numerical calculations for solving the optimization problem based on local interaction model and within the of continuum mechanics. A comparison of the optimal bodies with absolutely optimal bodies possessing the minimum resistance of penetration below which is impossible to achieve under given constraints on the geometry. It is shown that the conical striker with a variable vertex angle, which equal to the angle of the solution is absolutely optimal body of minimum resistance of penetration for each value of the velocity of implementation will have a final depth of penetration is only 12% more than the traditional body absolutely optimal maximum depth penetration.

Jig-Shape Optimization of a Low-Boom Supersonic Aircraft

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi

2018-01-01

A simple approach for optimizing the jig-shape is proposed in this study. This simple approach is based on an unconstrained optimization problem and applied to a low-boom supersonic aircraft. In this study, the jig-shape optimization is performed using the two-step approach. First, starting design variables are computed using the least-squares surface fitting technique. Next, the jig-shape is further tuned using a numerical optimization procedure based on an in-house object-oriented optimization tool. During the numerical optimization procedure, a design jig-shape is determined by the baseline jig-shape and basis functions. A total of 12 symmetric mode shapes of the cruise-weight configuration, rigid pitch shape, rigid left and right stabilator rotation shapes, and a residual shape are selected as sixteen basis functions. After three optimization runs, the trim shape error distribution is improved, and the maximum trim shape error of 0.9844 inches of the starting configuration becomes 0.00367 inch by the end of the third optimization run.

Generalized bipartite quantum state discrimination problems with sequential measurements

NASA Astrophysics Data System (ADS)

Nakahira, Kenji; Kato, Kentaro; Usuda, Tsuyoshi Sasaki

2018-02-01

We investigate an optimization problem of finding quantum sequential measurements, which forms a wide class of state discrimination problems with the restriction that only local operations and one-way classical communication are allowed. Sequential measurements from Alice to Bob on a bipartite system are considered. Using the fact that the optimization problem can be formulated as a problem with only Alice's measurement and is convex programming, we derive its dual problem and necessary and sufficient conditions for an optimal solution. Our results are applicable to various practical optimization criteria, including the Bayes criterion, the Neyman-Pearson criterion, and the minimax criterion. In the setting of the problem of finding an optimal global measurement, its dual problem and necessary and sufficient conditions for an optimal solution have been widely used to obtain analytical and numerical expressions for optimal solutions. Similarly, our results are useful to obtain analytical and numerical expressions for optimal sequential measurements. Examples in which our results can be used to obtain an analytical expression for an optimal sequential measurement are provided.

Optimal control on bladder cancer growth model with BCG immunotherapy and chemotherapy

NASA Astrophysics Data System (ADS)

Dewi, C.; Trisilowati

2015-03-01

In this paper, an optimal control model of the growth of bladder cancer with BCG (Basil Calmate Guerin) immunotherapy and chemotherapy is discussed. The purpose of this optimal control is to determine the number of BCG vaccine and drug should be given during treatment such that the growth of bladder cancer cells can be suppressed. Optimal control is obtained by applying Pontryagin principle. Furthermore, the optimal control problem is solved numerically using Forward-Backward Sweep method. Numerical simulations show the effectiveness of the vaccine and drug in controlling the growth of cancer cells. Hence, it can reduce the number of cancer cells that is not infected with BCG as well as minimize the cost of the treatment.

Mono and multi-objective optimization techniques applied to a large range of industrial test cases using Metamodel assisted Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Fourment, Lionel; Ducloux, Richard; Marie, Stéphane; Ejday, Mohsen; Monnereau, Dominique; Massé, Thomas; Montmitonnet, Pierre

2010-06-01

The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of "master points". Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples demonstrate the method versatility. They include billet shape optimization of a common rail, the cogging of a bar and a wire drawing problem.

Optimization methods and silicon solar cell numerical models

NASA Technical Reports Server (NTRS)

Girardini, K.

1986-01-01

The goal of this project is the development of an optimization algorithm for use with a solar cell model. It is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junctions depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm has been developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAPID). SCAPID uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the operation of a solar cell. A major obstacle is that the numerical methods used in SCAPID require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the value associated with the maximum efficiency. This problem has been alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution. Adapting SCAPID so that it could be called iteratively by the optimization code provided another means of reducing the cpu time required to complete an optimization. Instead of calculating the entire I-V curve, as is usually done in SCAPID, only the efficiency is calculated (maximum power voltage and current) and the solution from previous calculations is used to initiate the next solution.

A versatile multi-objective FLUKA optimization using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Vlachoudis, Vasilis; Antoniucci, Guido Arnau; Mathot, Serge; Kozlowska, Wioletta Sandra; Vretenar, Maurizio

2017-09-01

Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.