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1

Hybrid Evolutionary Multi-Objective Optimization Algorithms

-objective genetic local search (MOGLS) algorithm, which is the hybridization of a simple EMO algorithm with local search for multi- objective optimization was first implemented in [6], [7] as a multi-objective genetic by modifying the selection mechanism for choosing parent solutions for crossover in its EMO part

Coello, Carlos A. Coello

2

Hybrid Evolutionary Algorithm for Solving Global Optimization Problems

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid

Radha Thangaraj; Millie Pant; Ajith Abraham; Youakim Badr

2009-01-01

3

Improved hybrid optimization algorithm for 3D protein structure prediction.

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

4

A Hybrid Genetic Algorithm for Routing Optimization in IP Networks

engineering are demonstrated. Keywords Â IP Traffic engineering, genetic algorithm, bandwidth-delay sensitiveA Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay traffic engineering, which relies on conventional, destination-based routing protocols. We introduce

Riedl, Anton

5

A Hybrid Search Algorithm for Porous Air Bearings Optimization

The study deals with the development of a hybrid search algorithm for efficient optimization of porous air bearings. Both the compressible Reynolds equation and Darcy's law are linearized and solved iteratively by a successive-over-relaxation method for modeling parallel-surface porous bearings. Three factors affecting the computational efficiency of the numerical model are highlighted and discussed. The hybrid optimization is performed by

Nenzi Wang; Yau-Zen Chang

2002-01-01

6

A Hybrid Ant Colony Algorithm for Loading Pattern Optimization

NASA Astrophysics Data System (ADS)

Electricité de France (EDF) operates 58 nuclear power plant (NPP), of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R&D has developed automatic optimization tools that assist the experts. The latter can resort, for instance, to a loading pattern optimization software based on ant colony algorithm. This paper presents an analysis of the search space of a few realistic loading pattern optimization problems. This analysis leads us to introduce a hybrid algorithm based on ant colony and a local search method. We then show that this new algorithm is able to generate loading patterns of good quality.

Hoareau, F.

2014-06-01

7

A hybrid IWO\\/PSO algorithm for fast and global optimization

This paper presents a hybrid optimization algorithm which originates from Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Based on the novel and distinct qualifications of IWO and PSO, we introduce IWO\\/PSO algorithm and try to combine their excellent features in this extended algorithm. The efficiency of this algorithm both in the case of speed of convergence and optimality

Hossein Hajimirsadeghi; Caro Lucas

2009-01-01

8

In this article, a hybrid optimization method has been proposed consisting of Adaptive Genetic Algorithms (AGAs) and Constrained Nonlinear Programming (NLP) to solve the problems of performance optimization of circular array antenna consist of parallel center feeding short dipoles elements with two complex nonlinear optimization problems. In the first problem, the hybrid optimization algorithm is used to reduce the value

Ali Abdulhadi Noaman; Abdul Kareem S. Abdallah; Ramzy S. Ali

2010-01-01

9

Good elevator group control system would collect more information from the passengers, and make the system perform better. In this paper, a new hybrid elevator group control system with full digital keypads is proposed, on which immune particle swarm optimization (IPSO) hybrid algorithm is applied. Particle swarm optimization (PSO) algorithm has the advantages of simple model, fast convergence, and can

Fei Luo; Xiaolan Lin; Yuge Xu; Huijuan Li

2008-01-01

10

Previous work in antenna optimization has primarily focused on applications of optimization algorithms in conjunction with problem-specific or semi-analytic tools. However, previous developments in fast algorithms now offer the possibility of designs and moreover allow for full flexibility in material specification across three dimensions. As an example, this paper combines genetic algorithms (GA) and simulated annealing (SA) with fast hybrid

Zhifang Li; Yunus E. Erdemli; John L. Volakis; Panos Y. Papalambros

2002-01-01

11

Gradient, Non-Gradient and Hybrid Algorithms for Optimizing 3D Forging Sequences with Uncertainties

in order to minimize the potential of fold formation. Keywords: Optimization Algorithm, Response Surface be substituted to the exact evaluations of the objective function within costly global algorithmsGradient, Non-Gradient and Hybrid Algorithms for Optimizing 3D Forging Sequences with Uncertainties

Paris-Sud XI, UniversitÃ© de

12

A homogeneous superconducting magnet design using a hybrid optimization algorithm

NASA Astrophysics Data System (ADS)

This paper employs a hybrid optimization algorithm with a combination of linear programming (LP) and nonlinear programming (NLP) to design the highly homogeneous superconducting magnets for magnetic resonance imaging (MRI). The whole work is divided into two stages. The first LP stage provides a global optimal current map with several non-zero current clusters, and the mathematical model for the LP was updated by taking into account the maximum axial and radial magnetic field strength limitations. In the second NLP stage, the non-zero current clusters were discretized into practical solenoids. The superconducting conductor consumption was set as the objective function both in the LP and NLP stages to minimize the construction cost. In addition, the peak-peak homogeneity over the volume of imaging (VOI), the scope of 5 Gauss fringe field, and maximum magnetic field strength within superconducting coils were set as constraints. The detailed design process for a dedicated 3.0 T animal MRI scanner was presented. The homogeneous magnet produces a magnetic field quality of 6.0 ppm peak-peak homogeneity over a 16 cm by 18 cm elliptical VOI, and the 5 Gauss fringe field was limited within a 1.5 m by 2.0 m elliptical region.

Ni, Zhipeng; Wang, Qiuliang; Liu, Feng; Yan, Luguang

2013-12-01

13

This paper discusses optimal algorithms for closed-loop control of hybrid stepper motor drives and their microprocessor implementation. The torque characteristics and the optimal control angle of hybrid stepper motor drives with added series resistance and reluctant stepper motor drives have been described in detail in the literature. The specific contribution of the paper to this field of research consists of

P. Crnosija; Branislav Kuzmanovic; S. Ajdukovic

2000-01-01

14

Novel elevator group control system would collect more information from the passengers, and make the system perform better. In this paper, a novel hybrid destination registration elevator group control system is proposed, on which artificial immune optimization(AIO) algorithm is applied. Artificial immune optimization uses high cytometaplasia in optimization can avoid local minima and accelerate the optimization. The application of artificial

Yuge Xu; Fei Luo; Xiaolan Lin

2010-01-01

15

New Hybrid Optimization Algorithms for Machine Scheduling Problems

decision (or constraint satisfaction) problems resulting from a dichotomy to locate the .... is allowed to start at time rj; once started, the job will occupy the machine ...... the theory and applications of large-scale optimization algorithms, discrete.

2006-12-03

16

This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on

Morteza Montazeri-Gh; Amir Poursamad; Babak Ghalichi

2006-01-01

17

NASA Astrophysics Data System (ADS)

Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.

Igeta, Hideki; Hasegawa, Mikio

18

Hybrid algorithms based on harmony search and differential evolution for global optimization

In this paper, two hybrid algorithms are proposed for global optimization by merging the mechanisms of Harmony Search (HS) and Differential Evolution (DE). First, the learning mechanism of a variant of HS named Global-best Harmony Search (GHS) is embedded into the framework of DE to develop an algorithm called Global Harmony Differential Evolution (GHDE). Besides, the differential operator of DE

Ling-po Li; Ling Wang

2009-01-01

19

Optimal Economical Sizing Of A PV-Wind Hybrid Energy System Using Genetic Algorithm

In this paper, a new formulation for optimizing the design of a photovoltaic (PV)-wind hybrid energy home system, incorporating a storage battery, is developed. This formulation is carried out with the purpose of arriving at a selection of the system economical components that can reliably satisfy the load demand. Genetic algorithm (GA) optimization technique is utilized to satisfy two purposes.

Abd El-Shafy A. Nafeh

2011-01-01

20

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. PMID:24688370

Yu, Xiaobing; Cao, Jie; Shan, Haiyan; Zhu, Li; Guo, Jun

2014-01-01

21

Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

Lamarckian learning has been introduced into evolutionary computation as local search mechanism. The relevant research topic,\\u000a memetic computation, has received significant amount of interests. In this study, a novel Lamarckian learning strategy is\\u000a designed for improving the Nondominated Neighbor Immune Algorithm, a novel hybrid multi-objective optimization algorithm,\\u000a Multi-objective Lamarckian Immune Algorithm (MLIA), is proposed. The Lamarckian learning performs a greedy

Maoguo Gong; Chao Liu; Licheng Jiao; Gang Cheng

2010-01-01

22

NASA Astrophysics Data System (ADS)

The dynamically dimensioned search (DDS) continuous global optimization algorithm by Tolson and Shoemaker (2007) is modified to solve discrete, single-objective, constrained water distribution system (WDS) design problems. The new global optimization algorithm for WDS optimization is called hybrid discrete dynamically dimensioned search (HD-DDS) and combines two local search heuristics with a discrete DDS search strategy adapted from the continuous DDS algorithm. The main advantage of the HD-DDS algorithm compared with other heuristic global optimization algorithms, such as genetic and ant colony algorithms, is that its searching capability (i.e., the ability to find near globally optimal solutions) is as good, if not better, while being significantly more computationally efficient. The algorithm's computational efficiency is due to a number of factors, including the fact that it is not a population-based algorithm and only requires computationally expensive hydraulic simulations to be conducted for a fraction of the solutions evaluated. This paper introduces and evaluates the algorithm by comparing its performance with that of three other algorithms (specific versions of the genetic algorithm, ant colony optimization, and particle swarm optimization) on four WDS case studies (21- to 454-dimensional optimization problems) on which these algorithms have been found to perform well. The results obtained indicate that the HD-DDS algorithm outperforms the state-of-the-art existing algorithms in terms of searching ability and computational efficiency. In addition, the algorithm is easier to use, as it does not require any parameter tuning and automatically adjusts its search to find good solutions given the available computational budget.

Tolson, Bryan A.; Asadzadeh, Masoud; Maier, Holger R.; Zecchin, Aaron

2009-12-01

23

A hybrid algorithm of evolutionary optimization, called hybrid differential evolution (HDE), is developed in this study. The acceleration phase and migration phase are embedded into the original algorithm of differential evolution (DE). These two phases are used to improve the convergence speed without decreasing the diversity among individuals. With some assumptions, this hybrid method is shown as a method using

Ji-Pyng Chiou; Feng-Sheng Wang

1999-01-01

24

A hybrid optimization technique coupling an evolutionary and a local search algorithm

NASA Astrophysics Data System (ADS)

Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case.

Kelner, Vincent; Capitanescu, Florin; Leonard, Olivier; Wehenkel, Louis

2008-06-01

25

An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137

Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan

2013-01-01

26

An effective hybrid firefly algorithm with harmony search for global numerical optimization.

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137

Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan

2013-01-01

27

Particle Swarm Optimization(PSO) algorithm has been wiedly used in many areas due to the advantages of simple realization and fast convergence.While it will trap in local minimum easily. To overcome the shortcoming, this paper proposes a hybrid algorithm PSO-SA by introducing the simulated annealing(SA) algorithm to the standard PSO and applies it to hybrid elevator group control system for optimizing

Luo Fei; Zhao Xiaocui; Xu Yuge

2010-01-01

28

Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization.

Many real-life optimization problems often face an increased rank of nonsmoothness (many local minima) which could prevent a search algorithm from moving toward the global solution. Evolution-based algorithms try to deal with this issue. The algorithm proposed in this paper is called GAAPI and is a hybridization between two optimization techniques: a special class of ant colony optimization for continuous domains entitled API and a genetic algorithm (GA). The algorithm adopts the downhill behavior of API (a key characteristic of optimization algorithms) and the good spreading in the solution space of the GA. A probabilistic approach and an empirical comparison study are presented to prove the convergence of the proposed method in solving different classes of complex global continuous optimization problems. Numerical results are reported and compared to the existing results in the literature to validate the feasibility and the effectiveness of the proposed method. The proposed algorithm is shown to be effective and efficient for most of the test functions. PMID:21896393

Ciornei, Irina; Kyriakides, Elias

2012-02-01

29

A Robust and Efficient Hybrid Algorithm for Global Optimization

The objective of realizing more effective solution during any complex system design can be achieved by the application of Multidisciplinary Design Optimization. The primary problem in developing an integrated framework, which is essential in the iterative procedure of optimization, is how to automate the design codes that were designed to be used by experts. Automation of design codes primarily calls

C. Geethaikrishnan; P. M. Mujumdar; K. Sudhakar; V. Adimurthy

2009-01-01

30

NASA Astrophysics Data System (ADS)

A hybrid numerical algorithm combining the Gauss Pseudospectral Method (GPM) with a Generalized Polynomial Chaos (gPC) method to solve nonlinear stochastic optimal control problems with constraint uncertainties is presented. TheGPM and gPC have been shown to be spectrally accurate numerical methods for solving deterministic optimal control problems and stochastic differential equations, respectively. The gPC uses collocation nodes to sample the random space, which are then inserted into the differential equations and solved by applying standard differential equation methods. The resulting set of deterministic solutions is used to characterize the distribution of the solution by constructing a polynomial representation of the output as a function of uncertain parameters. Optimal control problems are especially challenging to solve since they often include path constraints, bounded controls, boundary conditions, and require solutions that minimize a cost functional. Adding random parameters can make these problems even more challenging. The hybrid algorithm presented in this dissertation is the first time the GPM and gPC algorithms have been combined to solve optimal control problems with random parameters. Using the GPM in the gPC construct provides minimum cost deterministic solutions used in stochastic computations that meet path, control, and boundary constraints, thus extending current gPC methods to be applicable to stochastic optimal control problems. The hybrid GPM-gPC algorithm was applied to two concept demonstration problems: a nonlinear optimal control problem with multiplicative uncertain elements and a trajectory optimization problem simulating an aircraft flying through a threat field where exact locations of the threats are unknown. The results show that the expected value, variance, and covariance statistics of the polynomial output function approximations of the state, control, cost, and terminal time variables agree with Monte-Carlo simulation results while requiring on the order of (1/40)th to (1/100)th the number of collocation points and computation time. It was shown that the hybrid algorithm demonstrated an ability to effectively characterize how the solutions to optimization problems vary with uncertainty, and has the potential with continued development and availability of more powerful computer workstations, to be a powerful tool applicable to more complex control problems of interest to the Department of Defense.

Cottrill, Gerald C.

31

A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem

NASA Astrophysics Data System (ADS)

Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.

Kumar, Sandeep; Kumar Sharma, Vivek; Kumari, Rajani

2013-11-01

32

In this work, the optimal adjustment algorithm for p coordinates, which arose from a generalization of the optimal pair adjustment algorithm is used to accelerate the convergence of interior point methods using a hybrid iterative approach for solving the linear systems of the interior point method. Its main advantages are simplicity and fast initial convergence. At each interior point iteration,

Carla T. L. S. Ghidini; A. R. L. Oliveira; Jair. Silva; M. I. Velazco

33

\\u000a Electric power distribution networks are large-scale infrastructures that need to be planned regularly and operated continuously.\\u000a The planning and operation tasks involve difficult decision-making processes that can be formulated as optimization problems:\\u000a large-scale combinatorial optimization problems. These problems have been addressed successfully with specially designed evolutionary\\u000a hybrid approaches. Such approaches rely upon Lamarckian evolutionary hybrid algorithms. In this chapter, we

Pedro M. S. Carvalho; Luis A. F. M. Ferreira

34

In this article, a new hybrid evolutionary algorithm (HEA) is proposed to determine the optimal placement of multi-type FACTS devices for simultaneously maximizing the total transfer capability (TTC) and minimizing system real power losses of power transfers between different control areas. Multi-objective optimal power flow (OPF) with FACTS devices including TTC, system real power loss and penalty functions is used

Peerapol Jirapong; Weerakorn Ongsakul

2007-01-01

35

NASA Astrophysics Data System (ADS)

In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.

Baran, Ismet; Tutum, Cem C.; Hattel, Jesper H.

2013-08-01

36

NASA Astrophysics Data System (ADS)

In this study, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to neighborhood step size. The NPTSGA is developed on the thought of integrating genetic algorithm (GA) with a TS based MOEA, niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arose from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA can balance the tradeoff between the intensification of nondomination and the diversification of near Pareto-optimal solutions and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

Wu, J.; Yang, Y.; Wu, J.

2011-12-01

37

In this paper the optimization of a Tubular Permanent Magnet-Linear Generator (TPM-LiG) for energy generation is presented. The application is related to the sea wave energy generation for small sensorized buoy. The optimization process is developed by means of an hybrid evolutionary algorithm widely presented in the paper. The advantage of this algorithm is in the wide exploration of the

Andrea Pirisi; Marco Mussetta; Giambattista Gruosso; Riccardo Enrico Zich

2010-01-01

38

TUNING A HYBRID OPTIMIZATION ALGORITHM BY DETERMINING THE MODALITY OF THE DESIGN SPACE

In this paper we present an approach for increasing the efficiency of a hybrid Genetic\\/Sequential Linear Programming algorithm. We introduce two metrics for evaluating the modality of the design space and then use this information to efficiently switch between the Genetic Algorithm and SLP algorithm. The motivation for this study is an effort to reduce the computational expense associated with

Kurt Hacker; John Eddy; Kemper Lewis

39

NASA Astrophysics Data System (ADS)

The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.

2012-05-01

40

GENETIC ALGORITHMS Kurt A. Hacker Postdoctoral Research Associate AIAA Student Member Dept. of Mechanical algorithms such as Simulated Annealing or Genetic Algorithms often can locate near optimal solutions but can (CFD), heat transfer and vehicle dynamics simulations. The execution time for these types of analyses

Lewis, Kemper E.

41

NASA Astrophysics Data System (ADS)

Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures.

Lian, Jijian; He, Longjun; Ma, Bin; Li, Huokun; Peng, Wenxiang

2013-09-01

42

A hybrid simplex differential evolution algorithm

According to the disadvantage of slow convergence rate of the basic differential evolution (DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the basic DE algorithm is presented in this paper. This hybrid procedure performed the exploration with DE and the exploitation with the NM simplex method. Sensitivity to the control parameters of the proposed

Lianghong Wu; Yaonan Wang; Xiaofang Yuan; Shaowu Zhou

2010-01-01

43

A genetic algorithm aiming the optimal design of composite structures under non-linear behaviour is presented. The approach\\u000a addresses the optimal material\\/stacking sequence in laminate construction and material distribution topology in composite\\u000a structures as a multimodal optimization problem. The proposed evolutionary process is based on a sequential hierarchical relation\\u000a between subpopulations evolving in separated isolation stages followed by migration. Improvements based

C. A. Conceição António

2006-01-01

44

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design.

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctive-use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water-delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources. PMID:21635245

Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter

2012-01-01

45

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctive-use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water-delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources. ?? 2011, National Ground Water Association.

Chiu, Y.-C.; Nishikawa, T.; Martin, P.

2012-01-01

46

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctiveuse strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources.

Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter

2012-01-01

47

The particle swarm optimization (PSO) was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence

Amir Mohammadi; Mostafa Jazaeri

2007-01-01

48

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

Salehi, Mojtaba

2010-01-01

49

NASA Astrophysics Data System (ADS)

This paper presents a novel example-based super-resolution (SR) algorithm with improved k-means cluster. In this algorithm, genetic k-means (GKM) with hybrid particle swarm optimization (HPSO) is employed to improve the reconstruction of high-resolution (HR) images, and a pre-processing of classification in frequency is used to accelerate the procedure. Self-redundancy across different scales of a natural image is also utilized to build attached training set to expand example-based information. Meanwhile, a reconstruction algorithm based on hybrid supervise locally linear embedding (HSLLE) is proposed which uses training sets, high-resolution images and self-redundancy across different scales of a natural image. Experimental results show that patches are classified rapidly in training set processing session and the runtime of reconstruction is half of traditional algorithm at least in super-resolution session. And clustering and attached training set lead to a better recovery of low-resolution (LR) image.

Feng, Kunpeng; Zhou, Tong; Cui, Jiwen; Tan, Jiubin

2014-11-01

50

Broadband and broad-angle low-scattering metasurface based on hybrid optimization algorithm.

A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves. PMID:25089367

Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun

2014-01-01

51

Broadband and Broad-Angle Low-Scattering Metasurface Based on Hybrid Optimization Algorithm

NASA Astrophysics Data System (ADS)

A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves.

Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun

2014-08-01

52

Broadband and Broad-Angle Low-Scattering Metasurface Based on Hybrid Optimization Algorithm

A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves. PMID:25089367

Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun

2014-01-01

53

NASA Astrophysics Data System (ADS)

A reliable seismic-resistant design of structures is achieved in accordance with the seismic design codes by designing structures under seven or more pairs of earthquake records. Based on the recommendations of seismic design codes, the average time-history responses (ATHR) of structure is required. This paper focuses on the optimal seismic design of reinforced concrete (RC) structures against ten earthquake records using a hybrid of particle swarm optimization algorithm and an intelligent regression model (IRM). In order to reduce the computational time of optimization procedure due to the computational efforts of time-history analyses, IRM is proposed to accurately predict ATHR of structures. The proposed IRM consists of the combination of the subtractive algorithm (SA), K-means clustering approach and wavelet weighted least squares support vector machine (WWLS-SVM). To predict ATHR of structures, first, the input-output samples of structures are classified by SA and K-means clustering approach. Then, WWLS-SVM is trained with few samples and high accuracy for each cluster. 9- and 18-storey RC frames are designed optimally to illustrate the effectiveness and practicality of the proposed IRM. The numerical results demonstrate the efficiency and computational advantages of IRM for optimal design of structures subjected to time-history earthquake loads.

Gharehbaghi, Sadjad; Khatibinia, Mohsen

2015-03-01

54

A MULTI-OBJECTIVE EVOLUTIONARY HYBRID OPTIMIZER

A new hybrid multi-objective, multivariable optimizer utilizing Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Differential Evolution (NSDE), and Multi-Objective Particle Swarm (MOPSO) has been created and tested. The optimizer features automatic switching among these algorithms to expedite the convergence of the optimal Pareto front in the objective function(s) space. The ultimate goal of using such a hybrid optimizer is to

George S. Dulikravich; Ramon J. Moral; Debasis Sahoo

2005-01-01

55

NASA Astrophysics Data System (ADS)

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

56

HOPSPACK: Hybrid Optimization Parallel Search Package.

In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel SearchPackage), a new software platform which facilitates combining multiple optimization routines into asingle, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The frameworkis designed such that existing optimization source code can be easily incorporated with minimalcode modification. By maintaining the integrity of each individual solver, the strengths and codesophistication of the original optimization package are retained and exploited.4

Gray, Genetha A.; Kolda, Tamara G.; Griffin, Joshua; Taddy, Matt; Martinez-Canales, Monica

2008-12-01

57

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective

Guohui Zhang; Xinyu Shao; Peigen Li; Liang Gao

2009-01-01

58

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather\\u000a complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence\\u000a of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction

Mojtaba Salehi; Ardeshir Bahreininejad

59

A hybrid discrete Artificial Bee Colony - GRASP algorithm for clustering

This paper presents a new hybrid algorithm, which is based on the concepts of the artificial bee colony (ABC) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines an artificial bee colony optimization algorithm for the solution of the feature selection problem and a

Y. Marinakis; M. Marinaki; N. Matsatsinis

2009-01-01

60

NASA Astrophysics Data System (ADS)

In order to identify the source term of gas emission in atmosphere, an improved hybrid algorithm combined with the minimum relative entropy (MRE) and particle swarm optimization (PSO) method was presented. Not only are the estimated source parameters obtained, but also the confidence intervals at some probability levels. If only the source strength was required to be determined, the problem can be viewed as a linear inverse problem directly, which can be solved by original MRE method successfully. When both source strength and location are unknown, the common gas dispersion model should be transformed to be a linear system. Although the transformed linear model has some differences from that in original MRE method, satisfied estimation results were still obtained by adding iteratively adaptive adjustment parameters in the MRE-PSO method. The dependence of the MRE-PSO method on prior information such as lower and upper bound, prior expected values and noises were also discussed. The results showed that the confidence intervals and estimated parameters are influenced little by the prior bounds and expected values, but the errors affect the estimation results greatly. The simulation and experiment verification results showed that the MRE-PSO method is able to identify the source parameters with satisfied results. Finally, the error model was probed and then it was added in the MRE-PSO method. The addition of error model improves the performance of the identification method. Therefore, the MRE-PSO method with adjustment parameters proposed in this paper is a potential good method to resolve inverse problem in atmosphere environment.

Ma, Denglong; Wang, Simin; Zhang, Zaoxiao

2014-09-01

61

NASA Astrophysics Data System (ADS)

In this paper, a novel robust watermarking technique using particle swarm optimization and k-nearest neighbor algorithm is introduced to protect the intellectual property rights of color images in the spatial domain. In the embedding process, the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into the individual blocks. Then, in order to extract the embedded watermark, features are obtained from watermark embedded blocks using the symmetric cross-shape kernel. These features are used to generate two centroids belonging to each binary (1 and 0) value of the watermark implementing particle swarm optimization. Subsequently, the embedded watermark is extracted by evaluating these centroids utilizing k-nearest neighbor algorithm. According to the test results, embedded watermark is extracted successfully even if the watermarked image is exposed to various image processing attacks.

F?nd?k, O?uz; Babao?lu, ?smail; Ülker, Erkan

2010-12-01

62

Fireworks Algorithm for Optimization

\\u000a Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed\\u000a for global optimization of complex functions. In the proposed FA, two types of explosion (search) processes are employed,\\u000a and the mechanisms for keeping diversity of sparks are also well designed. In order to demonstrate the validation of the FA,\\u000a a number of experiments were

Ying Tan; Yuanchun Zhu

2010-01-01

63

A hybrid genetic algorithm for resolving closely spaced objects

NASA Technical Reports Server (NTRS)

A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

64

Genetic algorithm and particle swarm optimization combined with Powell method

NASA Astrophysics Data System (ADS)

In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

2013-10-01

65

Hybrid algorithm in phase diversity wavefront sensing

NASA Astrophysics Data System (ADS)

Phase diversity (PD), proposed by Gonsalves, is a kind of wavefront sensing technique based on measurement of two or more images of object. The optical system involved is relatively simple. It makes use of the methods of optimization and image processing, which can jointly estimate phase aberration as well as object itself simultaneously. The most significant characteristic of this technique is that it works well with extended scenes. Steepest descent method and conjugate gradient method both are preferable algorithms for nonlinear optimization. As a matter of fact, any one of two methods has some limitations. Steepest descent method is a local property and conjugate gradient method's convergence rate is slow. Combining two methods to develop a mixed algorithm, we can avoid entrapping into a local minimum and raise global convergence rate. Simulation results demonstrate that the hybrid algorithm has the features of quick convergence rate, comparatively large convergence range, which make the method of phase diversity remarkably robust and numerically efficient.

Wang, Xin; Zhao, Dazun; Mao, Heng; Wang, Xiao

2009-05-01

66

A Hybrid Differential Invasive Weed Algorithm for Congestion Management

NASA Astrophysics Data System (ADS)

This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).

Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam

67

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

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

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

68

Hybrid algorithms for fuzzy reverse supply chain network design.

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

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

69

An Algorithmic Framework for Multiobjective Optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

70

An algorithmic framework for multiobjective optimization.

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

71

A Hybrid Engine Control System based on Genetic Algorithms

In this paper an optimal management of the energetic flows in a hybrid vehicle based on a Genetic Algorithm is introduced. The aim is maximize the use of the electric engine, minimizing the use of the internal combustion one, increasing the driving pleasure and reducing consumptions, emissions and noise. From the available literature, a typical configuration series-parallel hybrid engine as

D. PORTO; A. MARTINEZ; S. SCIMONE

72

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics. PMID:20064026

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

73

A new hybrid genetic algorithm based on chaos and PSO

In practice, two key problems have been found in genetic algorithm (GA), one is premature convergence and the other is weak local search ability. In this paper, a new hybrid genetic algorithm based on chaos and particle swarm optimization (PSO) is proposed to solve the two problems above. The basic principle is that chaotic search mechanism and PSO mutation are

Yiwen Wang; Min Yao

2009-01-01

74

The Algorithmic Analysis of Hybrid Systems

We present a general framework for the formal specification and algorithmic analysis of hybrid systems. A hybrid system consists of a discrete program with an analog environment. We model hybrid systems as finite automata equipped with variables that evolve continuously with time according to dynamical laws. For verification purposes, we restrict ourselves to linear hybrid systems, where all variables follow

Rajeev Alur; Costas Courcoubetis; Nicolas Halbwachs; Thomas A. Henzinger; Pei-hsin Ho; Xavier Nicollin; Alfredo Olivero; Joseph Sifakis; Sergio Yovine

1995-01-01

75

AOAB -- Automated Optimization Algorithm Benchmarking

In this paper we present AOAB, the Automated Optimization Algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different benchmarks with different parameter settings. Based on the results, AOAB can automatically perform comparisons between different algorithms and settings. It can aid the researcher to identify trends for good parameter settings

Thomas Weise; Li Niu; Ke Tang

2010-01-01

76

Hybrid Ant Algorithm and Applications for Vehicle Routing Problem

NASA Astrophysics Data System (ADS)

Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.

Xiao, Zhang; Jiang-qing, Wang

77

Most protocols for multi-party computation (MPC) are secure either against information-theoretic (IT) or against computationally bounded adversaries. Hybrid-secure MPC protocols guarantee different levels of security, depending on the power of the adversary. We present a hybrid-secure MPC protocol that provides an optimal trade-off between IT robustness and computational privacy: For any robustness parameter < n 2 we obtain an MPC

Christoph Lucas; Dominik Raub; Ueli Maurer

78

A number of game strategies have been developed in past decades and used in the fields of economics, engi- neering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high

DongSeop Lee; Luis Felipe Gonzalez; Jacques Périaux; Karkenahalli Srinivas

2011-01-01

79

Concurrent genetic algorithms for optimization of large structures

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high-rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the

Hojjat Adeli; Nai-Tsang Cheng

1994-01-01

80

Hybrid Genetic Algorithms for Feature Selection

This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and

Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon

2004-01-01

81

Meeting the goals of space situational awareness requires the capabilities of imaging objects in space in the visible light hand at high resolutions and tracking their positions and orbits. This paper summarizes the determination of designs for a hybrid constellation consisting of two types of satellites to provide these capabilities in the vicinity of equatorial GEO. This is cast as

Eugene Fahnestock; Richard Scott Erwin

2005-01-01

82

The Rational Hybrid Monte Carlo Algorithm

The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.

M. A. Clark

2006-10-06

83

HYBRID FAST HANKEL TRANSFORM ALGORITHM FOR ELECTROMAGNETIC MODELING

A hybrid fast Hankel transform algorithm has been developed that uses several complementary features of two existing algorithms: Anderson's digital filtering or fast Hankel transform (FHT) algorithm and Chave's quadrature and continued fraction algorithm. A hybrid FHT subprogram ...

84

Optimal nonmonotonic convergence of the iterative Fourier-transform algorithm

NASA Astrophysics Data System (ADS)

The increase of the monotonic convergence rate is an important issue for iterative Fourier-transform algorithms. However, the steepest monotonic convergence of the iterative Fourier-transform algorithm does not always promise an optimal solution in the design of a diffractive optical element. The optimal nonmonotonic convergence of the iterative Fourier-transform algorithm is investigated by employing a microgenetic algorithm. The proposed hybrid scheme of the iterative Fourier-transform algorithm and the microgenetic algorithm show nonmonotonic convergence, and this results in a superior design.

Kim, Hwi; Lee, Byoungho

2005-02-01

85

Optimizing Hybrid Spreading in Metapopulations

Epidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information, and computer viruses. Epidemics can spread by \\textit{local spreading}, where infected nodes can only infect a limited set of direct target nodes and \\textit{global spreading}, where an infected node can infect every other node. In reality, many epidemics spread using a hybrid mixture of both types of spreading. In this study we develop a theoretical framework for studying hybrid epidemics, and examine the optimum balance between spreading mechanisms in terms of achieving the maximum outbreak size. In a metapopulation, made up of many weakly connected subpopulations, we show that one can calculate an optimal tradeoff between local and global spreading which will maximise the extent of the epidemic. As an example we analyse the 2008 outbreak of the Internet worm Conficker, which uses hybrid spreading to propagate through the internet. Our results suggests that the worm would have been eve...

Zhang, Changwang; Cox, Ingemar J; Chain, Benjamin M

2014-01-01

86

Most scheduling problems are complex combinatorial problems and very difficult to solve [Manage. Sci. 35 (1989) 164; F.S. Hillier, G.J. Lieberman, Introduction to Operations Research, Holden-Day, San Francisco, CA, 1967]. That is why, lots of methods focus on the optimization according to a single criterion (makespan, workloads of machines, waiting times, etc.). The combining of several criteria induces additional complexity

Imed Kacem; Slim Hammadi; Pierre Borne

2002-01-01

87

Optimal Control for a Parallel Hybrid Hydraulic Excavator Using Particle Swarm Optimization

Optimal control using particle swarm optimization (PSO) is put forward in a parallel hybrid hydraulic excavator (PHHE). A power-train mathematical model of PHHE is illustrated along with the analysis of components' parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators. PMID:23818832

Wang, Dong-yun; Guan, Chen

2013-01-01

88

Parameterless Hierarchical Bayesian Optimization Algorithm

the parameterless populationsizing scheme can be incorporated into other estimation of distribution algorithms]. That is why the dream of having a fully parameterless optimizer for the entire class of nearly decomposable

Peinke, Joachim

89

Fairness in optimal routing algorithms

FAIRNESS IN OPTIMAL ROUTING ALGORITHMS A Thesis by JEFFREY ALAN GOOS Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfiHment of the requirements for the degree of MASTER OF SCIENCE December 1988 Major... Subject: Electrical Engineering FAIRNESS IN OPTIMAL ROUTING ALGORITHMS A Thesis by JEFFREY ALAN GOOS Approved as to style and content by: Wei K. Tsai (Co-Chairman of Committee) C Pierce E. Cantrell (Co-Chairman of Committee) Jer D. Gibson...

Goos, Jeffrey Alan

1988-01-01

90

NASA Astrophysics Data System (ADS)

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

Zheng, Genrang; Lin, ZhengChun

91

Differential Evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. Biogeography-Based Optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE\\/BBO, for the global numerical optimization

Wenyin Gong; Zhihua Cai; Charles X. Ling

2010-01-01

92

Fast Hybrid Algorithms for PET Image Reconstruction

Fast Hybrid Algorithms for PET Image Reconstruction Quanzheng Li, Student Member, IEEE, Sangtae Ahn, Member, IEEE, and Richard Leahy, Fellow, IEEE, Abstract-- We describe a hybrid approach to iterative PET estimates convergence behavior for each method by fitting an exponential to the objective function

Leahy, Richard M.

93

PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC

NASA Astrophysics Data System (ADS)

A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSO-CMAC and CMAC feed-forward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSO-CMAC algorithm.

Tu, Ji; Cao, Shaozhong

94

Ant Algorithms for Discrete Optimization

Ant Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 Universit@iridia.ulb.ac.be Luca M. Gambardella IDSIA Corso Elvezia 36 CH-6900 Lugano Switzerland luca@idsia.ch Keywords ant algorithms, ant colony optimiza- tion, swarm intelligence, metaheuris- tics, natural computation Abstract

Hutter, Frank

95

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

96

Algorithms for bilevel optimization

NASA Technical Reports Server (NTRS)

General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

97

Phylogenetically Acquired Representations and Hybrid Evolutionary Algorithms

wozniak@isc.cnrs.fr First, we explain why Genetic Algorithms (GAs), inspired by the Modern Synthesis, do of the paper, we propose a hybrid version of genetic algorithms, differently organizing the flow of genetic information by introducing inheritance of acquired traits and Horizontal Gene Transfer, a good tool for handle

Paris-Sud XI, Université de

98

Fitting PAC spectra with a hybrid algorithm

NASA Astrophysics Data System (ADS)

A hybrid algorithm (HA) that blends features of genetic algorithms (GA) and simulated annealing (SA) was implemented for simultaneous fits of perturbed angular correlation (PAC) spectra. The main characteristic of the HA is the incorporation of a selection criterion based on SA into the basic structure of GA. The results obtained with the HA compare favorably with fits performed with conventional methods.

Alves, M. A.; Carbonari, A. W.

99

Fitting PAC spectra with a hybrid algorithm

NASA Astrophysics Data System (ADS)

A hybrid algorithm (HA) that blends features of genetic algorithms (GA) and simulated annealing (SA) was implemented for simultaneous fits of perturbed angular correlation (PAC) spectra. The main characteristic of the HA is the incorporation of a selection criterion based on SA into the basic structure of GA. The results obtained with the HA compare favorably with fits performed with conventional methods.

Alves, M. A.; Carbonari, A. W.

2008-01-01

100

Bayesian network structure learning using chaos hybrid genetic algorithm

NASA Astrophysics Data System (ADS)

A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

101

Constrained Multiobjective Biogeography Optimization Algorithm

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591

Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping

2014-01-01

102

Optimizing the specificity of nucleic acid hybridization

The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse

Sherry Xi Chen; David Yu Zhang; Peng Yin

2012-01-01

103

A novel hybrid algorithm for scheduling steel-making continuous casting production

In this paper, steel-making continuous casting (SCC) scheduling problem (SCCSP) is investigated. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making. Since classic optimization methods fail to obtain an optimal solution for this problem over a suitable time, a novel iterative algorithm is developed. The proposed algorithm, named HANO, is based

Arezoo Atighehchian; Mehdi Bijari; Hamed Tarkesh

2009-01-01

104

Multilevel algorithms for nonlinear optimization

NASA Technical Reports Server (NTRS)

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

105

Genetic algorithm optimization of entanglement

We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation

Jorge C. Navarro-Munoz; H. C. Rosu; R. Lopez-Sandoval

2006-11-13

106

A Hybrid Evolutionary Algorithm for Wheat Blending Problem

This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed. PMID:24707222

Bonyadi, Mohammad Reza; Michalewicz, Zbigniew; Barone, Luigi

2014-01-01

107

Firefly Algorithm, Lévy Flights and Global Optimization

NASA Astrophysics Data System (ADS)

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Yang, Xin-She

108

Size optimization of space trusses using Big Bang–Big Crunch algorithm

A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.

A. Kaveh; S. Talatahari

2009-01-01

109

An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems

This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment (TUC) problem\\u000a in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in\\u000a modern power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the\\u000a objective function

Wei Li; Hao-yu Peng; Wei-hang Zhu; De-ren Sheng; Jian-hong Chen

2009-01-01

110

For the cooling system of plastic injection mold affects significantly the productivity and quality of the finial products, the cooling system design is of great importance. In this paper, a hybrid approach combining particle swarm optimization (PSO) and genetic algorithms (GA) is developed to achieve the cooling system optimal design. Based on the finite element method (FEM) and the finite

Li Ren; WenXiao Zhang

2011-01-01

111

A New Optimized GA-RBF Neural Network Algorithm

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666

Zhao, Dean; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

112

HYBRID RUNGE-KUTTA AND QUASI-NEWTON METHODS FOR UNCONSTRAINED NONLINEAR OPTIMIZATION

HYBRID RUNGE-KUTTA AND QUASI-NEWTON METHODS FOR UNCONSTRAINED NONLINEAR OPTIMIZATION by Darin in unconstrained non- linear optimization while Runge-Kutta methods are widely used for the numerical integration of ODEs. In this thesis, hybrid algorithms combining low-order implicit Runge-Kutta methods for gradient

Jay, Laurent O.

113

Optimal Control of Hybrid Systems in Air Traffic Applications

NASA Astrophysics Data System (ADS)

Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient implementation of the proposed algorithms.

Kamgarpour, Maryam

114

Two-dimensional phase unwrapping using a hybrid genetic algorithm.

A novel hybrid genetic algorithm (HGA) is proposed to solve the branch-cut phase unwrapping problem. It employs both local and global search methods. The local search is implemented by using the nearest-neighbor method, whereas the global search is performed by using the genetic algorithm. The branch-cut phase unwrapping problem [a nondeterministic polynomial (NP-hard) problem] is implemented in a similar way to the traveling-salesman problem, a very-well-known combinational optimization problem with profound research and applications. The performance of the proposed algorithm was tested on both simulated and real wrapped phase maps. The HGA is found to be robust and fast compared with three well-known branch-cut phase unwrapping algorithms. PMID:17279161

Karout, Salah A; Gdeisat, Munther A; Burton, David R; Lalor, Michael J

2007-02-10

115

Genetic Algorithm for Optimization: Preprocessor and Algorithm

NASA Technical Reports Server (NTRS)

Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

Sen, S. K.; Shaykhian, Gholam A.

2006-01-01

116

Optimizing the specificity of nucleic acid hybridization

Optimizing the specificity of nucleic acid hybridization David Yu Zhang1,2 *, Sherry Xi Chen3, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature to 37 88888C, from 1 mM Mg21 to 47 mM Mg21 , and with nucleic acid concentrations from 1 nM to 5 m

Zhang, David Yu

117

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

NASA Technical Reports Server (NTRS)

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

118

Hybrid Evolutionary Algorithms and Clustering Search

A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local\\u000a search only in actually promising search areas. The inspiration in nature has been pursued to design flexible, coherent, and\\u000a efficient computational models. In this chapter, the clustering search (*CS) is proposed as a generic way of combining search\\u000a metaheuristics with clustering

Alexandre C. M. Oliveira; Luiz A. N. Lorena

119

Evolutionary optimization algorithm by entropic sampling

NASA Astrophysics Data System (ADS)

A combinatorial optimization algorithm, genetic-entropic algorithm, is proposed. This optimization algorithm is based on the genetic algorithms and the natural selection via entropic sampling. With the entropic sampling, this algorithm helps to escape local optima in the complex optimization problems. To test the performance of the algorithm, we adopt the NK model (N is the number of bits in the string and K is the degree of epistasis) and compare the performances of the proposed algorithm with those of the canonical genetic algorithm. It is found that the higher the K value, the better this algorithm can escape local optima and search near global optimum. The characteristics of this algorithm in terms of the power spectrum analysis together with the difference between two algorithms are discussed.

Lee, Chang-Yong; Han, Seung Kee

1998-03-01

120

Path planning using a hybrid evolutionary algorithm based on tree structure encoding.

A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389

Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn

2014-01-01

121

Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding

A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the “dummy node” is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389

Wang, Siao-En; Guo, Jian-Horn

2014-01-01

122

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

123

Global optimization algorithms for a CAD workstation

This paper describes two new versions of the controlled random search procedure for global optimization (CRS). Designed primarily to suit the user of a CAD workstation, these algorithms can also be used effectively in other contexts. The first, known as CRS3, speeds the final convergence of the optimization by combining a local optimization algorithm with the global search procedure. The

W. L. Price

1987-01-01

124

Multiobjective muffler shape optimization with hybrid acoustics modeling.

This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design. PMID:21895077

Airaksinen, Tuomas; Heikkola, Erkki

2011-09-01

125

A Hybrid ACO\\/PSO Control Algorithm for Distributed Swarm Robots

In this paper, we present a hybrid Ant Colony Optimization\\/Particle Swarm Optimization (ACO\\/PSO) control algorithm for distributed swarm robots, where each robot can only communicate with its neighbors within its communication range. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. This hybrid ACO\\/PSO architecture adopts the feedback mechanism from environment of ACO and

Juan C. Muller

2009-01-01

126

A Hybrid ACO\\/PSO Control Algorithm for Distributed Swarm Robots

In this paper, we present a hybrid ant colony optimization\\/particle swarm optimization (ACO\\/PSO) control algorithm for distributed swarm robots, where each robot can only communicate with its neighbors within its communication range. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. This hybrid ACO\\/PSO architecture adopts the feedback mechanism from environment of ACO and

Yan Meng; O. Kazeem; J. C. Muller

2007-01-01

127

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

NASA Astrophysics Data System (ADS)

The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.

Baruk?i?, Marinko; Nikolovski, Srete; Jovi?, Franjo

2010-11-01

128

Evolutionary programming based optimal power flow algorithm

This paper develops an efficient and reliable evolutionary programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems,

Jason Yuryevich; Kit Po Wong

1999-01-01

129

Global optimization of hybrid systems

Systems that exhibit both discrete state and continuous state dynamics are called hybrid systems. In most nontrivial cases, these two aspects of system behavior interact to such a significant extent that they cannot be ...

Lee, Cha Kun

2006-01-01

130

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 to 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.

Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China)] [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China)] [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China) [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)

2014-03-15

131

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 to 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. PMID:24697395

Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

2014-03-01

132

A hybrid of the genetic algorithm and concurrent simplex

THE GENETIC ALGORITHM A. The Innards of the Genetic Algorithm. . . 1. A Toy Problem 2. The Works 3. A Second Toy Problem B. The Effectiveness of the Genetic Algorithm . C. Previous Genetic Algorithm Hybrids 1. Pipelining Hybrids . 2. Abstraction... will introduce an example problem; later that problem will be used to illustrate how a genetic algorithm arrives at its solutions. 1. A Toy Problem Consider the eight queens puzzle, a well ? known problem in chess. In the game of chess, the most powerful...

Randolph, David Ethan

1995-01-01

133

Hybrid algorithms in quantum Monte Carlo

NASA Astrophysics Data System (ADS)

With advances in algorithms and growing computing powers, quantum Monte Carlo (QMC) methods have become a leading contender for high accuracy calculations for the electronic structure of realistic systems. The performance gain on recent HPC systems is largely driven by increasing parallelism: the number of compute cores of a SMP and the number of SMPs have been going up, as the Top500 list attests. However, the available memory as well as the communication and memory bandwidth per element has not kept pace with the increasing parallelism. This severely limits the applicability of QMC and the problem size it can handle. OpenMP/MPI hybrid programming provides applications with simple but effective solutions to overcome efficiency and scalability bottlenecks on large-scale clusters based on multi/many-core SMPs. We discuss the design and implementation of hybrid methods in QMCPACK and analyze its performance on current HPC platforms characterized by various memory and communication hierarchies.

Kim, Jeongnim; Esler, Kenneth P.; McMinis, Jeremy; Morales, Miguel A.; Clark, Bryan K.; Shulenburger, Luke; Ceperley, David M.

2012-12-01

134

Hybrid ECAL: Optimization and Related Developments

Hybrid ECAL is a cost-conscious option of electromagnetic calorimeter (ECAL) for particle flow calorimetry to be used in a detector of International Linear Collider (ILC). It is a combination of silicon-tungsten ECAL, which realizes high granularity and robust measurement of electromagnetic shower, and scintillator-tungsten ECAL, which gives affordable cost with similar performance to silicon. Optimization and a data acquisition trial in a test bench for the hybrid ECAL are described in this article.

Suehara, T; Sumida, H; Ueno, H; Sudo, Y; Yoshioka, T; Kawagoe, K

2015-01-01

135

Hybrid ECAL: Optimization and Related Developments

Hybrid ECAL is a cost-conscious option of electromagnetic calorimeter (ECAL) for particle flow calorimetry to be used in a detector of International Linear Collider (ILC). It is a combination of silicon-tungsten ECAL, which realizes high granularity and robust measurement of electromagnetic shower, and scintillator-tungsten ECAL, which gives affordable cost with similar performance to silicon. Optimization and a data acquisition trial in a test bench for the hybrid ECAL are described in this article.

T. Suehara; H. Hirai; H. Sumida; H. Ueno; Y. Sudo; T. Yoshioka; K. Kawagoe

2015-03-30

136

Optimal Control of a Mechanical Hybrid Powertrain

This paper presents the design of an optimal energy management strategy (EMS) for a low-cost mechanical hybrid powertrain. It uses mechanical components only—a flywheel, clutches, gears, and a continuously variable transmission—for its hybrid functionalities of brake energy recuperation, reduction of inefficient part-load operation of the engine, and engine shutoff during vehicle standstill. This powertrain has mechanical characteristics, such as a

Koos van Berkel; Theo Hofman; Bas Vroemen; Maarten Steinbuch

2012-01-01

137

Improved Clonal Selection Algorithm Combined with Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

Gao, Shangce; Wang, Wei; Dai, Hongwei; Li, Fangjia; Tang, Zheng

138

SOME RECENT DEVELOPMENTS IN NONLINEAR OPTIMIZATION ALGORITHMS

This article provides a condensed overview of some of the major today's features (both classical or recently developed), used in the design and development of algorithms to solve nonlinear continuous optimization problems. We rst consider the unconstrained optimization case to introduce the line-search and trust-region approaches as globalization techniques to force an algorithm to converge from any starting point. We

A. Sartenaer

2003-01-01

139

Solving combinatorial optimization problems using Karmarkar's algorithm

We describe a cutting plane algorithm for solving combinatorial optimization problems. The primal projective standard-form variant of Karmarkar's algorithm for linear programming is applied to the duals of a sequence of linear programming relaxations of the combinatorial optimization problem.

John E. Mitchell; Michael J. Todd

1992-01-01

140

Optimal control of parallel hybrid electric vehicles

In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. The methodology developed is valid for those problem that are characterized

Antonio Sciarretta; Michael Back; Lino Guzzella

2004-01-01

141

Intelligent perturbation algorithms for space scheduling optimization

NASA Technical Reports Server (NTRS)

Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.

Kurtzman, Clifford R.

1991-01-01

142

Hybrid optimal control Notes for CIRA course, Bertinoro 2003.

Hybrid optimal control Notes for CIRA course, Bertinoro 2003. Benedetto Piccoli # Abstract. These notes are devoted to the use of some analytical tools for hybrid optimal control. After introducing basic notations for a quite general class of hybrid systems, we state both the Hybrid Maximum Principle

Piccoli, Benedetto

143

Water Distribution System Optimization Using Genetic Simulated Annealing Algorithm

\\u000a Water supply system optimization makes use of the latest advances in hybrid genetic algorithm to automatically determine the\\u000a least-cost pump operation for each pump station in large-scale water distribution system while satisfying simplified hydraulic\\u000a performance requirements. Calibration results of the original model were pretty good. The comparison results show that the\\u000a difference between the simplified and the original mode simulation

Shihu. Shu

2011-01-01

144

Intelligent perturbation algorithms to space scheduling optimization

NASA Technical Reports Server (NTRS)

The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.

Kurtzman, Clifford R.

1991-01-01

145

Traffic sharing algorithms for hybrid mobile networks

NASA Technical Reports Server (NTRS)

In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

Arcand, S.; Murthy, K. M. S.; Hafez, R.

1995-01-01

146

Parametric optimization of hybrid car engines (Final version, published in Optimization and

the problem of optimal design of hybrid car engines which combine thermal and electric power. The optimalParametric optimization of hybrid car engines (Final version, published in Optimization a nonconvex nonsmooth optimization problem with a bundle method. Keywords: Hybrid car engines, bilevel

Bonnans, FrÃ©dÃ©ric

147

Optimization methods applied to hybrid vehicle design

NASA Technical Reports Server (NTRS)

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Donoghue, J. F.; Burghart, J. H.

1983-01-01

148

Hybrid metrology universal engine: co-optimization

NASA Astrophysics Data System (ADS)

In recent years Hybrid Metrology has emerged as an option for enhancing the performance of existing measurement toolsets and is currently implemented in production1. Hybrid Metrology is the practice to combine measurements from multiple toolset types in order to enable or improve the measurement of one or more critical parameters. While all applications tried before were improved through standard (sequential) hybridization of data from one toolset to another, advances in device architecture, materials and processes made possible to find one case that demanded a much deeper understanding of the physical basis of measurements and simultaneous optimization of data. This paper presents the first such work using the concept of co-optimization based hybridization, where image analysis parameters of CD-SEM (critical dimensions Scanning Electron Microscope) are modulated by profile information from OCD (optical critical dimension - scatterometry) while the OCD extracted profile is concurrently optimized through addition of the CD-SEM CD results. Test vehicle utilized in this work is the 14nm technology node based FinFET High-k/Interfacial layer structure.

Vaid, Alok; Osorio, Carmen; Tsai, Jamie; Bozdog, Cornel; Sendelbach, Matthew; Grubner, Eyal; Koret, Roy; Wolfling, Shay

2014-04-01

149

Optimization of hybrid solar dryer

The hybrid solar convective drying system considered here consists of a solar air heater, drying chamber, and electric heater to provide air at constant temperature to the dryer. In order to reduce the electric energy consumed, pebble bed storage was used, comprising one unit with drying chamber. Computer modeling and simulation were carried out to analyze the effect of design dimensions of the air heater and pebble storage bed on energy savings. Measurements of hourly weather conditions were used in the simulation to determine the optimum design dimensions that would realize minimum electric energy for each operating temperature. The energy saved for the four seasons of the year was obtained at different tilt angels of the air heater to discern the best tilt for each drying season, realizing minimum energy consumption.

Khattab, N.M. [Solar Energy Dept., Cairo (Egypt)

1996-10-01

150

Design of optimal correlation filters for hybrid vision systems

NASA Technical Reports Server (NTRS)

Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.

Rajan, Periasamy K.

1990-01-01

151

Optimal sizing of stand-alone hybrid wind\\/PV system with battery storage

In this paper, a new methodology developed to design a hybrid wind\\/photovoltaic (wind\\/PV) system, is presented. Based on an optimization process using a deterministic algorithm, the developed methodology helps the authors to obtain the optimal number and type of PV panels, wind turbines and storage units ensuring that the system total cost is minimized while guaranteeing the permanent availability of

Rachid BELFKIRA; Omessad HAJJI; Cristian NICHITA; Georges BARAKAT

2007-01-01

152

This paper presents a hybrid method for the synthesis and optimization of heat exchanger networks, which includes detailed design of heat exchangers. This task is achieved by combining the pinch design method with mathematical programming techniques, together with an optimal design algorithm of shell and tube heat exchangers based on the rigorous Bell-Delaware method. As result, the stream pressure drops

J. M. García; J. M. Ponce; M. Serna

2006-01-01

153

A hybrid optimization method of evolutionary and gradient search

NASA Astrophysics Data System (ADS)

This article proposes a hybrid optimization algorithm, which combines evolutionary algorithms (EA) and the gradient search technique, for optimization with continuous parameters. Inheriting the advantages of the two approaches, the new method is fast and capable of global search. The main structure of the new method is similar to that of EA except that a special individual called the gradient individual is introduced and EA individuals are located symmetrically. The gradient individual is propagated through generations by means of the quasi-Newton method. Gradient information required for the quasi-Newton method is calculated from the costs of EA individuals produced by the evolution strategies (ES). The symmetric placement of the individuals with respect to the best individual is for calculating the gradient vector by the central difference method. For the estimation of the inverse Hessian matrix, symmetric Rank-1 update shows better performance than BFGS and DFP. Numerical tests on various benchmark problems and a practical control design example demonstrate that the new hybrid algorithm gives a faster convergence rate than EA, without sacrificing the capability of global search.

Tahk, Min-Jea; Woo, Hyun-Wook; Park, Moon-Su

2007-01-01

154

Adaptable optimization : theory and algorithms

Optimization under uncertainty is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed ...

Caramanis, Constantine (Constantine Michael), 1977-

2006-01-01

155

Solving Fuzzy Optimization Problem Using Hybrid Ls-Sa Method

NASA Astrophysics Data System (ADS)

Fuzzy optimization problem has been one of the most and prominent topics inside the broad area of computational intelligent. It's especially relevant in the filed of fuzzy non-linear programming. It's application as well as practical realization can been seen in all the real world problems. In this paper a large scale non-linear fuzzy programming problem has been solved by hybrid optimization techniques of Line Search (LS), Simulated Annealing (SA) and Pattern Search (PS). As industrial production planning problem with cubic objective function, 8 decision variables and 29 constraints has been solved successfully using LS-SA-PS hybrid optimization techniques. The computational results for the objective function respect to vagueness factor and level of satisfaction has been provided in the form of 2D and 3D plots. The outcome is very promising and strongly suggests that the hybrid LS-SA-PS algorithm is very efficient and productive in solving the large scale non-linear fuzzy programming problem.

Vasant, Pandian

2011-06-01

156

Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971

2014-01-01

157

An Intelligent Auction Scheme for Smart Grid Market Using a Hybrid Immune Algorithm

Smart Grid technology is recognized as a key com- ponent of the solution to challenges such as the increasing electric demand, an aging utility infrastructure and workforce, and the environmental impact of greenhouse gases produced during elec- tric generation. This paper presents the application of a hybrid optimization algorithm for distributed energy resource (DER) management in Smart Grid operation. The

Bhuvaneswari Ramachandran; Sanjeev K. Srivastava; Chris S. Edrington; David A. Cartes

2011-01-01

158

Optimal mesh algorithms for VLSI routing

Optimal mesh algorithms are developed for several VLSI routing problems, such as river routing between rectangles, routing within a rectilinear polygon, and wiring module pins to frame pads. It is assumed that the mesh consists of ?n×?n processors, where n is the input size. Each processor has a constant amount of memory. All the algorithms run in time O(?n ).

Shing-Chong Chang; J. JaJa

1988-01-01

159

APPLYING NEW OPTIMIZATION ALGORITHMS TO MODEL PREDICTIVE

, developments in optimization, and esÂ pecially in interiorÂpoint methods, have produced a new set of algorithms the interiorÂpoint algoÂ rithm and show how it can be applied efficiently to the problem (1). We then move new algorithms for quadratic programming can be applied efficiently to this problem. The approach

Wright, Steve

160

Ant Algorithms Solve Difficult Optimization Problems

Ant Algorithms Solve Difficult Optimization Problems Marco Dorigo IRIDIA Universit´e Libre de Bruxelles 50 Avenue F. Roosevelt B-1050 Brussels, Belgium mdorigo@ulb.ac.be Abstract. The ant algorithms research field builds on the idea that the study of the behavior of ant colonies or other social insects

Libre de Bruxelles, Université

161

Finding Tradeoffs by Using Multiobjective Optimization Algorithms

The objective of the present study is to demonstrate performances of Evolutionary Algorithms (EAs) and conventional gradient-based methods for finding Pareto fronts. The multiobjective optimization algorithms are applied to analytical test problems as well as to the real-world problems of a compressor design. The comparison results clearly indicate the superiority of EAs in finding tradeoffs.

Shigeru Obayashi; Daisuke Sasaki; Akira Oyama

2005-01-01

162

Evolutionary Algorithm for Optimal Vaccination Scheme

NASA Astrophysics Data System (ADS)

The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease.

Parousis-Orthodoxou, K. J.; Vlachos, D. S.

2014-03-01

163

NASA Astrophysics Data System (ADS)

This research investigates the performance of bi-level hybrid optimal control algorithms in the solution of minimum delta-velocity geostationary transfer maneuvers with cooperative en-route inspection. The maneuvers, introduced here for the first time, are designed to populate a geostationary constellation of space situational awareness satellites while providing additional characterization of objects in lower-altitude orbit regimes. The maneuvering satellite, called the chaser, performs a transfer from low Earth orbit to geostationary orbit, during which it performs an inspection of one of several orbiting targets in conjunction with a ground site for the duration of the target's line-of-site contact with that site. A three-target scenario is used to test the performance of multiple bi-level hybrid optimal control algorithms. A bi-level hybrid algorithm is then utilized to solve fifteen-, and thirty-target scenarios and shown to have increasing benefit to complete enumeration as the number of targets is increased. Results indicate that the en-route inspection can be accomplished for a small increase in the delta-velocity required for a simple transfer to geostationary orbit given the same initial conditions.

Showalter, Daniel J.; Black, Jonathan T.

2014-12-01

164

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

NASA Astrophysics Data System (ADS)

The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.

Salajegheh, Eysa; Gholizadeh, Saeed; Khatibinia, Mohsen

2008-03-01

165

Distributed evolutionary algorithms for simulation optimization

The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. In this paper, the use of evolutionary algorithms is suggested for the optimization of simulation models. Several types of variables are taken into account. The reduction of computing cost is achieved through the parallelization of this method, which allows several simulation experiments to be run

Henri Pierreval; Jean-luc Paris

2000-01-01

166

Randomized Algorithm for UAVs Group Flight Optimization

Randomized Algorithm for UAVs Group Flight Optimization Konstantin Amelin Natalia Amelina , Oleg: The problem of small UAVs flight optimization is considered. To solve this problem thermal updrafts are used. For the precise detection of the thermal updrafts center the simultaneous perturbation stochastic approximation

Granichin, Oleg

167

Algorithms for optimal dyadic decision trees

A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory

2009-01-01

168

Two stochastic optimization algorithms applied to nuclear reactor core design

Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck's Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and

Wagner F. Sacco; Cassiano R. E. de oliveira; Cláudio M. N. A. Pereira

2006-01-01

169

An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.

Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; therebymaking our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a wellconverged and well-diversified approximation of the Pareto front. PMID:25373137

Bechikh, Slim; Chaabani, Abir; Said, Lamjed Ben

2014-10-30

170

Parallel Algorithms for Graph Optimization using Tree Decompositions

Although many NP-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of required dynamic programming tables and excessive running times of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree-decomposition based approach to solve maximum weighted independent set. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

Weerapurage, Dinesh P [ORNL; Sullivan, Blair D [ORNL; Groer, Christopher S [ORNL

2013-01-01

171

Parallel Algorithms for Graph Optimization using Tree Decompositions

Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL; Groer, Christopher S [ORNL

2012-06-01

172

A novel bee swarm optimization algorithm for numerical function optimization

NASA Astrophysics Data System (ADS)

The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.

Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush

2010-10-01

173

Hybrid intelligent optimization methods for engineering problems

NASA Astrophysics Data System (ADS)

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles.

Pehlivanoglu, Yasin Volkan

174

Optimal parallel quantum query algorithms

We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a number of problems, specifically Theta((n/p)^{2/3}) p-parallel queries for element distinctness and Theta((n/p)^{k/(k+1)} for k-sum. Our upper bounds are obtained by parallelized quantum walk algorithms, and our lower bounds are based on a relatively small modification of the adversary lower bound method, combined with recent results of Belovs et al. on learning graphs. We also prove some general bounds, in particular that quantum and classical p-parallel complexity are polynomially related for all total functions f when p is small compared to f's block sensitivity.

Stacey Jeffery; Frederic Magniez; Ronald de Wolf

2015-02-20

175

Protein structure optimization with a "Lamarckian" ant colony algorithm.

We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian" genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms. PMID:24407312

Oakley, Mark T; Richardson, E Grace; Carr, Harriet; Johnston, Roy L

2013-01-01

176

Analysis and optimization of hybrid electric vehicle thermal management systems

NASA Astrophysics Data System (ADS)

In this study, the thermal management system of a hybrid electric vehicle is optimized using single and multi-objective evolutionary algorithms in order to maximize the exergy efficiency and minimize the cost and environmental impact of the system. The objective functions are defined and decision variables, along with their respective system constraints, are selected for the analysis. In the multi-objective optimization, a Pareto frontier is obtained and a single desirable optimal solution is selected based on LINMAP decision-making process. The corresponding solutions are compared against the exergetic, exergoeconomic and exergoenvironmental single objective optimization results. The results show that the exergy efficiency, total cost rate and environmental impact rate for the baseline system are determined to be 0.29, ¢28 h-1 and 77.3 mPts h-1 respectively. Moreover, based on the exergoeconomic optimization, 14% higher exergy efficiency and 5% lower cost can be achieved, compared to baseline parameters at an expense of a 14% increase in the environmental impact. Based on the exergoenvironmental optimization, a 13% higher exergy efficiency and 5% lower environmental impact can be achieved at the expense of a 27% increase in the total cost.

Hamut, H. S.; Dincer, I.; Naterer, G. F.

2014-02-01

177

A Cuckoo Search Algorithm for Multimodal Optimization

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850

2014-01-01

178

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

2015-01-01

179

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

2015-01-01

180

Optimal approximation algorithms for digital filter design

NASA Astrophysics Data System (ADS)

Several new algorithms are presented for the optimal approximation and design of various classes of digital filters. An iterative algorithm is developed for the efficient design of unconstrained and constrained infinite impulse response (IIR) digital filters. Both in the unconstrained and constrained cases, the numerator and denominator of the filter transfer function are designed iteratively by recourse to the Remez algorithm and to appropriate design parameters and criteria, at each iteration. This makes it possible for the algorithm to be implemented by means of a short main program which uses (at each iteration) the linear phase FIR filter design algorithm of McClellan et al. as a subroutine. The approach taken also permits the filter to be designed with a desired ripple ratio. Also, the algorithm determines automatically the minimum passband ripple corresponding to the prescribed orders and band edges of the filter. The filter is designed directly without guessing the passband ripple or stopband ripple.

Liang, J. K.

181

Optimal algorithms for approximate clustering

In a clustering problem, the aim is to partition a given set of n points in d-dimensional space into k groups, called clusters, so that points within each cluster are near each other. Two objective functions frequently used to measure the performance of a clustering algorithm are, for any L4 metric, (a) the maximum distance between pairs of points in

Tomás Feder; Daniel H. Greene

1988-01-01

182

The well-known particle swarm optimization (PSO) proposed by Kennedy and Eberhart has been widely applied to the continuous optimal problems. However, it is still intractable to apply PSO to discrete optimization problems, such as permutation flow shop scheduling problems (PFSSP). In this paper, a new high performing metaheuristic algorithm hybridizing PSO with variable neighborhood search (VNS) is proposed to solve

Y. Sun; M. Liu; C. Y. Zhang; L. Gao; K. L. Lian

2010-01-01

183

SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method

NASA Astrophysics Data System (ADS)

This study focuses on on-line specific fuel consumption (SFC) optimization of aero engines. For solving this optimization problem, a nonlinear pneumatic and thermodynamics model of the aero engine is built and a hybrid optimization technique which is formed by combining the genetic algorithm (GA) and the sequential quadratic programming (SQP) is presented. The ability of standard GA and standard SQP in solving this type of problem is investigated. It has been found that, although the SQP is fast, very little SFC reductions can be obtained. The GA is able to solve the problem well but a lot of computational time is needed. The presented hybrid GA-SQP gives a good SFC optimization effect and saves 76.6% computational time when compared to the standard GA. It has been shown that the hybrid GA-SQP is a more effective and higher real-time method for SFC on-line optimization of the aero engine.

Li, Jie; Fan, Ding; Sreeram, Victor

2013-12-01

184

NASA Astrophysics Data System (ADS)

In order to test the high dynamic range error beyond one wavelength after the rough polish process, we design a phase retrieval hybrid algorithm based on diffraction angular spectrum theory. Phase retrieval is a wave front sensing method that uses the intensity distribution to reconstruct the phase distribution of optical field. Phase retrieval is established on the model of diffractive propagation and approach the real intensity distribution gradually. In this paper, we introduce the basic principle and challenges of optical surface measurement using phase retrieval, then discuss the major parts of phase retrieval: diffractive propagation and hybrid algorithm. The angular spectrum theory describes the diffractive propagation in the frequency domain instead of spatial domain, which simplifies the computation greatly. Through the theoretical analysis, the angular spectrum in discrete form is more effective when the high frequency part values less and the diffractive distance isn't far. The phase retrieval hybrid algorithm derives from modified GS algorithm and conjugate gradient method, aiming to solve the problem of phase wrapping caused by the high dynamic range error. In the algorithm, phase distribution is described by Zernike polynomials and the coefficients of Zernike polynomials are optimized by the hybrid algorithm. Simulation results show that the retrieved phase distribution and real phase distribution are quite contiguous for the high dynamic range error beyond ?.

Feng, Liang; Zeng, Zhi-ge; Wu, Yong-qian

2013-08-01

185

Optimizing the specificity of nucleic acid hybridization

NASA Astrophysics Data System (ADS)

The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse temperature, salt and concentration conditions. We rationally designed ‘toehold exchange’ probes that approximate these properties, and comprehensively tested them against five different DNA targets and 55 spurious analogues with energetically representative single-base changes (replacements, deletions and insertions). These probes produced discrimination factors between 3 and 100+ (median, 26). Without retuning, our probes function robustly from 10 °C to 37 °C, from 1 mM Mg2+ to 47 mM Mg2+, and with nucleic acid concentrations from 1 nM to 5 µM. Experiments with RNA also showed effective single-base change discrimination.

Zhang, David Yu; Chen, Sherry Xi; Yin, Peng

2012-03-01

186

An algorithm for optimal water resources planning

AN ALGORITEM FOR OPTIMAL WATER RESOURCES PLANNING A Thesis By INDI1iKRI V. S. RAJU Submitted to the Graduate College of the Texas ASM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE January 1988... Major Subject: Industrial Engineering AN ALGORITHM FOR OPTIMAL WATER RESOURCES PLANNING A Thesis By INDUKURI V. S. RAJU Approved as to style and content by: (Chairman of Committee) Head of Department) (Member) (Member) January 1968...

Raju, Indukuri Venkata Satyanarayana

1968-01-01

187

A hybrid fast Hankel transform algorithm for electromagnetic modeling

A hybrid fast Hankel transform algorithm has been developed that uses several complementary features of two existing algorithms: Anderson's digital filtering or fast Hankel transform (FHT) algorithm and Chave's quadrature and continued fraction algorithm. A hybrid FHT subprogram (called HYBFHT) written in standard Fortran-77 provides a simple user interface to call either subalgorithm. The hybrid approach is an attempt to combine the best features of the two subalgorithms to minimize the user's coding requirements and to provide fast execution and good accuracy for a large class of electromagnetic problems involving various related Hankel transform sets with multiple arguments. Special cases of Hankel transforms of double-order and double-argument are discussed, where use of HYBFHT is shown to be advantageous for oscillatory kernal functions. -Author

Anderson, W.L.

1989-01-01

188

Algorithm Optimally Allocates Actuation of a Spacecraft

NASA Technical Reports Server (NTRS)

A report presents an algorithm that solves the following problem: Allocate the force and/or torque to be exerted by each thruster and reaction-wheel assembly on a spacecraft for best performance, defined as minimizing the error between (1) the total force and torque commanded by the spacecraft control system and (2) the total of forces and torques actually exerted by all the thrusters and reaction wheels. The algorithm incorporates the matrix vector relationship between (1) the total applied force and torque and (2) the individual actuator force and torque values. It takes account of such constraints as lower and upper limits on the force or torque that can be applied by a given actuator. The algorithm divides the aforementioned problem into two optimization problems that it solves sequentially. These problems are of a type, known in the art as semi-definite programming problems, that involve linear matrix inequalities. The algorithm incorporates, as sub-algorithms, prior algorithms that solve such optimization problems very efficiently. The algorithm affords the additional advantage that the solution requires the minimum rate of consumption of fuel for the given best performance.

Motaghedi, Shi

2007-01-01

189

Optimizing the Hybridization Factor for a Parallel Hybrid Electric Small Car

ADVISOR simulations of 75,100 and 125 kW total power, parallel hybrid small cars show that the hybridization factor (HF) giving maximum fuel efficiency (termed optimal HF) for the 75 kW small car is 0.49. For the 100 kW small car the optimal HF is 0.58. For the 125 kW small car, the optimal HF is 0.6. At these hybridization factors,

Courtney Holder; J. Gover

2006-01-01

190

Optimal Power Train Design of a Hybrid Refuse Collector Vehicle

components compared to a heuristically designed vehicle. Keywords: Hybrid Electric Vehicle (HEV), optimization, refuse collector vehicle I. INTRODUCTION Today, hybrid electric vehicles are accepted as a step is possible with most of hybrid electric vehicles, promises significant fuel saving. In addition

Paderborn, UniversitÃ¤t

191

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

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.

Crossley, William A.; Martin. Eric T.

2002-01-01

192

Wind Mill Pattern Optimization using Evolutionary Algorithms

Wind Mill Pattern Optimization using Evolutionary Algorithms Charlie Vanaret ENAC , IRIT 7 av Ed 31062 Toulouse Cedex 9, France jean-marc.alliot@irit.fr ABSTRACT When designing a wind farm layout, we a grid, we can gain up to 3% of energy output on simple exam- ples of wind farms dealing with many

193

Parallel Simulated Annealing Algorithms in Global Optimization

Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the assessment of various solution approaches

Esin Onba?o?lu; Linet Özdamar

2001-01-01

194

A Derivative Free Optimization Algorithm in Practice

). In this paper we discuss some particular applicaÂ tions from Boeing. One arises in helicopter rotor blade design In the paper we are considering a general derivaÂ tive free optimization (DFO) algorithm for minimizÂ ing one needs to minimize an objective measured by some experiment or by a complicated simulation package

Toint, Philippe

195

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Ozmutlu, H. Cenk

2014-01-01

196

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

197

Implementation and comparison of PSO-based algorithms for multi-modal optimization problems

NASA Astrophysics Data System (ADS)

This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration.

Sriyanyong, Pichet; Lu, Haiyan

2013-10-01

198

Algorithm for fixed-range optimal trajectories

NASA Technical Reports Server (NTRS)

An algorithm for synthesizing optimal aircraft trajectories for specified range was developed and implemented in a computer program written in FORTRAN IV. The algorithm, its computer implementation, and a set of example optimum trajectories for the Boeing 727-100 aircraft are described. The algorithm optimizes trajectories with respect to a cost function that is the weighted sum of fuel cost and time cost. The optimum trajectory consists at most of a three segments: climb, cruise, and descent. The climb and descent profiles are generated by integrating a simplified set of kinematic and dynamic equations wherein the total energy of the aircraft is the independent or time like variable. At each energy level the optimum airspeeds and thrust settings are obtained as the values that minimize the variational Hamiltonian. Although the emphasis is on an off-line, open-loop computation, eventually the most important application will be in an on-board flight management system.

Lee, H. Q.; Erzberger, H.

1980-01-01

199

Convergence Rates of Efficient Global Optimization Algorithms

Efficient global optimization is the problem of minimizing an unknown function f, using as few evaluations f(x) as possible. It can be considered as a continuum-armed bandit problem, with noiseless data and simple regret. Expected improvement is perhaps the most popular method for solving this problem; the algorithm performs well in experiments, but little is known about its theoretical properties. Implementing expected improvement requires a choice of Gaussian process prior, which determines an associated space of functions, its reproducing-kernel Hilbert space (RKHS). When the prior is fixed, expected improvement is known to converge on the minimum of any function in the RKHS. We begin by providing convergence rates for this procedure. The rates are optimal for functions of low smoothness, and we modify the algorithm to attain optimal rates for smoother functions. For practitioners, however, these results are somewhat misleading. Priors are typically not held fixed, but depend on parameters estimated from t...

Bull, Adam D

2011-01-01

200

Applications of Numerical Optimal Control to Nonlinear Hybrid Systems

that contains both autonomous and controlled switches; optimal and model predictive control solutions are given of the controllers. Key words: Hybrid systems, optimal control, numerical optimization, model predictive control- brid systems. In general, the problem is quite hard as it involves both elements of optimal control

Zefran, Milo?

201

Genetic Algorithm for Structural Optimization of Tubular Nanostructures

NASA Astrophysics Data System (ADS)

Why can metals or oxides form nanotubes, sometimes even chiral ones? How could silicon, which has little or no propensity for sp2 hybridization, form nanotubes akin with the well understood carbon nanotubes in which the atoms are unequivocally sp2 hybridized? It would be perhaps beneficial, if not expected, for the theory to step up to the plate and engage in the discovery of credible growth mechanisms and atomic structures for the tubular and multi-shell structures that can determine future directions in nanoscience and nanotechnology. In an effort to contribute to answering these questions, we present here a global optimization method designed specifically for tubular structures. Due to the recent success of the genetic algorithms in elucidating structures of 1- and 2-dimensional nanoscale materials, we base our optimization procedure on the same evolutionary principles. We have found that the cross-over operations based on planar cuts (which were so successful previously) are not sufficient to ensure convergence to lowest energy structures, and design new ones. The application of the new and more diverse cross-over operations has resulted in converged structures for different materials, which provides confidence in pursuing the application of genetic algorithm for finding the structures of new tubular materials.

Davies, Teresa E. B.; Popa, Mihail M.; Ciobanu, Cristian V.

2007-11-01

202

Optimized Vertex Method and Hybrid Reliability

NASA Technical Reports Server (NTRS)

A method of calculating the fuzzy response of a system is presented. This method, called the Optimized Vertex Method (OVM), is based upon the vertex method but requires considerably fewer function evaluations. The method is demonstrated by calculating the response membership function of strain-energy release rate for a bonded joint with a crack. The possibility of failure of the bonded joint was determined over a range of loads. After completing the possibilistic analysis, the possibilistic (fuzzy) membership functions were transformed to probability density functions and the probability of failure of the bonded joint was calculated. This approach is called a possibility-based hybrid reliability assessment. The possibility and probability of failure are presented and compared to a Monte Carlo Simulation (MCS) of the bonded joint.

Smith, Steven A.; Krishnamurthy, T.; Mason, B. H.

2002-01-01

203

A Novel Hybrid Algorithm for Task Graph Scheduling

One of the important problems in multiprocessor systems is Task Graph Scheduling. Task Graph Scheduling is an NP-Hard problem. Both learning automata and genetic algorithms are search tools which are used for solving many NP-Hard problems. In this paper a new hybrid method based on Genetic Algorithm and Learning Automata is proposed. The proposed algorithm begins with an initial population of randomly generated chromosomes and after some stages, each chromosome maps to an automaton. Experimental results show that superiority of the proposed algorithm over the current approaches.

Nezhad, Vahid Majid; Efimov, Evgueni

2011-01-01

204

A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

205

A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

206

?minimax: An Optimally Randomized MINIMAX Algorithm.

This paper proposes a simple extension of the celebrated MINIMAX algorithm used in zero-sum two-player games, called ?minimax. The ?minimax algorithm allows controlling the strength of an artificial rival by randomizing its strategy in an optimal way. In particular, the randomized shortest-path framework is applied for biasing the artificial intelligence (AI) adversary toward worse or better solutions, therefore controlling its strength. In other words, our model aims at introducing/implementing bounded rationality to the MINIMAX algorithm. This framework takes into account all possible strategies by computing an optimal tradeoff between exploration (quantified by the entropy spread in the tree) and exploitation (quantified by the expected cost to an end game) of the game tree. As opposed to other tree-exploration techniques, this new algorithm considers complete paths of a tree (strategies) where a given entropy is spread. The optimal randomized strategy is efficiently computed by means of a simple recurrence relation while keeping the same complexity as the original MINIMAX. As a result, the ?minimax implements a nondeterministic strength-adapted AI opponent for board games in a principled way, thus avoiding the assumption of complete rationality. Simulations on two common games show that ?minimax behaves as expected. PMID:22893439

García Díez, Silvia; Laforge, Jérôme; Saerens, Marco

2012-08-01

207

A reliable algorithm for optimal control synthesis

NASA Technical Reports Server (NTRS)

In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.

Vansteenwyk, Brett; Ly, Uy-Loi

1992-01-01

208

Hybrid stochastic optimization algorithms with line search

, University of Novi Sad, Trg Dositeja ObradoviÂ´ca 4, 21000 Novi Sad, Serbia, email:natasak@uns.ns.ac.yu, Research supported by Ministry of Science, Republic of Serbia, grant number 144006 2Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja ObradoviÂ´ca 6, 21000 Novi Sad, Serbia 1 #12;in practical

KrejiÃ¦, NataÂ?a

209

Hybrid Automata: An Algorithmic Approach to the Speci cation and Veri cation of

Hybrid Automata: An Algorithmic Approach to the Speci cation and Veri cation of Hybrid Systems1 of hybrid automata as a model and speci cation language for hybrid systems. Hybrid automatacan be viewed erential equations. We show that many of the examples considered in the workshop can be de ned by hybrid

Henzinger, Thomas A.

210

NASA Technical Reports Server (NTRS)

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Rogers, David

1991-01-01

211

Stroke volume optimization: the new hemodynamic algorithm.

Critical care practices have evolved to rely more on physical assessments for monitoring cardiac output and evaluating fluid volume status because these assessments are less invasive and more convenient to use than is a pulmonary artery catheter. Despite this trend, level of consciousness, central venous pressure, urine output, heart rate, and blood pressure remain assessments that are slow to be changed, potentially misleading, and often manifested as late indications of decreased cardiac output. The hemodynamic optimization strategy called stroke volume optimization might provide a proactive guide for clinicians to optimize a patient's status before late indications of a worsening condition occur. The evidence supporting use of the stroke volume optimization algorithm to treat hypovolemia is increasing. Many of the cardiac output monitor technologies today measure stroke volume, as well as the parameters that comprise stroke volume: preload, afterload, and contractility. PMID:25639574

Johnson, Alexander; Ahrens, Thomas

2015-02-01

212

Aimed at the shortcoming of neural network blind equalization algorithm, namely, the structure of neural network is difficult to determine, two basic principles of neural network blind equalization algorithm optimized by genetic algorithm were analyzed in the paper, by combining genetic algorithm and neural network blind equalization algorithm. At first, the structure and weight of neural network were optimized together

Liyi Zhang; Ting Liu; Yunshan Sun; Lei Chen

2010-01-01

213

Optimal energy management in series hybrid electric vehicles

This paper deals with the optimization of the instantaneous electrical generation\\/electrical storage power split in series hybrid electric vehicles (SHEV). Optimal energy management is related to the optimization of the instantaneous generation\\/storage power split in SHEV. Previously, a power split type solution of the series hybrid energy management problem has been attempted using a rule-based approach. Our approach performs a

A. Brahma; Y. Guezennec; G. Rizzoni

2000-01-01

214

DEACO: Hybrid Ant Colony Optimization with Differential Evolution

Ant colony optimization (ACO) algorithm is a novel meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behavior of real ant colonies. ACO has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In

Xiangyin Zhang; Haibin Duan; Jiqiang Jin

2008-01-01

215

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Deb, Suash; Yang, Xin-She

2014-01-01

216

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

217

Particle swarm optimization versus genetic algorithms for phased array synthesis

Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array

Daniel W. Boeringer; Douglas H. Werner

2004-01-01

218

Genetic Algorithms Compared to Other Techniques for Pipe Optimization

The genetic algorithm technique is a relatively new optimization tech- nique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization

Angus R. Simpson; Graeme C. Dandy; Laurence J. Murphy

1994-01-01

219

Multiobjective simulation optimization using an enhanced genetic algorithm

This paper presents an improved genetic algorithm ap- proach, based on new ranking strategy, to conduct multi- objective optimization of simulation modeling problems. This approach integrates a simulation model with stochas- tic nondomination-based multiobjective optimization tech- nique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of find- ing Pareto optimal solutions and its efficiency in

Hamidreza Eskandari; Luis Rabelo; Mansooreh Mollaghasemi

2005-01-01

220

The hybrid Monte Carlo Algorithm and the chiral transition

In this talk the author describes tests of the Hybrid Monte Carlo Algorithm for QCD done in collaboration with Greg Kilcup and Stephen Sharpe. We find that the acceptance in the glubal Metropolis step for Staggered fermions can be tuned and kept large without having to make the step-size prohibitively small. We present results for the finite temperature transition on 4/sup 4/ and 4 x 6/sup 3/ lattices using this algorithm.

Gupta, R.

1987-01-01

221

A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann

A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann Department of Mechanical-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine

Herrmann, Jeffrey W.

222

Intelligent perturbation algorithms for space scheduling optimization

NASA Technical Reports Server (NTRS)

The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.

Kurtzman, Clifford R.

1990-01-01

223

Hybrid protection algorithms based on game theory in multi-domain optical networks

NASA Astrophysics Data System (ADS)

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.

Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming

2011-12-01

224

Multidisciplinary design optimization using genetic algorithms

NASA Technical Reports Server (NTRS)

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.

Unal, Resit

1994-01-01

225

Parallel algorithm and hybrid regularization for dynamic PET reconstruction

Parallel algorithm and hybrid regularization for dynamic PET reconstruction N. Pustelnik, Student Abstract--To improve the estimation at the voxel level in dynamic Positron Emission Tomography (PET in the presence of Poisson noise and it is extended here to (dynamic) space + time PET image reconstruction

Boyer, Edmond

226

Hybrid Heuristic Algorithm for GPS Surveying Stefka Fidanova

. The purpose of surveying is to determine the locations of points on the earth. Measuring tapes or chains require that the survey crew physically pass through all the intervening terrain to measure the distanceHybrid Heuristic Algorithm for GPS Surveying Problem Stefka Fidanova IPP Â BAS, Acad. G. Bonchev

Fidanova, Stefka

227

In this paper a novel technique is proposed with which the optimal Degree of Hybridization (DOH) can be found with very little computational effort. This technique combines PSO algorithm and HEV simulation tool and its objective is to optimize both the fuel economy and vehicle performance and minimize the emissions, including HC, CO for a hybrid electric vehicle. Advanced VehIcle

Kazem Varesi; Ahmad Radan

2011-01-01

228

Gas pipeline optimization using adaptive algorithms

Transmission gas pipeline network consume significant amounts of energy. Then, minimizing the energy requirements is a challenging task. Due to the nonlinearity and poor knowledge of the system states, several results, based on the optimal control theory, are obtained only for simple configurations. In this paper an optimization scheme in the face of varying demand is carried out. It is based on the use of a dynamic simulation program as a plant model and the Pareto set technique to sell out useful experiments. Experiments are used for the identification of regression models based on an original class of functions. The nonlinear programming algorithm results. Its connection with regression models permits the definition off-line, and for a long time horizon, of the optimal discharge pressure trajectory for all the compressor stations. The use of adaptive algorithms, with high frequency, permits one to cancel the effect of unknown disturbances and errors in demand forecasts. In this way, an on-line optimization scheme using data of SCADA system is presented.

Smati, A.; Zemmour, N. [INH, Boumerdes (Algeria)

1996-12-31

229

Algorithms for optimizing CT fluence control

NASA Astrophysics Data System (ADS)

The ability to customize the incident x-ray fluence in CT via beam-shaping filters or mA modulation is known to improve image quality and/or reduce radiation dose. Previous work has shown that complete control of x-ray fluence (ray-by-ray fluence modulation) would further improve dose efficiency. While complete control of fluence is not currently possible, emerging concepts such as dynamic attenuators and inverse-geometry CT allow nearly complete control to be realized. Optimally using ray-by-ray fluence modulation requires solving a very high-dimensional optimization problem. Most optimization techniques fail or only provide approximate solutions. We present efficient algorithms for minimizing mean or peak variance given a fixed dose limit. The reductions in variance can easily be translated to reduction in dose, if the original variance met image quality requirements. For mean variance, a closed form solution is derived. The peak variance problem is recast as iterated, weighted mean variance minimization, and at each iteration it is possible to bound the distance to the optimal solution. We apply our algorithms in simulations of scans of the thorax and abdomen. Peak variance reductions of 45% and 65% are demonstrated in the abdomen and thorax, respectively, compared to a bowtie filter alone. Mean variance shows smaller gains (about 15%).

Hsieh, Scott S.; Pelc, Norbert J.

2014-03-01

230

Aeroelastic tailoring using piezoelectric actuation and hybrid optimization

Active control of fixed wing aircraft using piezoelectric materials has the potential to improve its aeroelastic response while reducing weight penalties. However, the design of active aircraft wings is a complex optimization problem requiring the use of formal optimization techniques. In this paper, a hybrid optimization procedure is applied to the design of an airplane wing, represented by a flat

Aditi Chattopadhyay; Charles E. Seeley; Ratneshwar Jha

1998-01-01

231

Aeroelastic tailoring using piezoelectric actuation and hybrid optimization

Active control of fixed wing aircraft using piezoelectric materials has the potential to improve its aeroelastic response while reducing weight penalties. However, the design of active aircraft wings is a complex optimization problem requiring the use of formal optimization techniques. In this paper, a hybrid optimization procedure is applied to the design of a scaled airplane wing model, represented by

Aditi Chattopadhyay; Charles E. Seeley; Ratneshwar Jha

1999-01-01

232

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969

Li, Shan; Zhao, Xing-Ming

2014-01-01

233

Global Optimization Algorithms – Theory and Application

Don, t print me! This book is much more useful as electronic resource: you can search, terms and click links. You can t do that in a printed book. The book is frequently updated and improved as well. Printed versions will just outdate. Also think about the trees that would have to die for the paper! Don, t print me! Preface This e-book is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered when trying to solve them. In this book, we focus on

Thomas Weise

234

Bell-Curve Based Evolutionary Optimization Algorithm

NASA Technical Reports Server (NTRS)

The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.

Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.

1998-01-01

235

Automated design of multiphase space missions using hybrid optimal control

NASA Astrophysics Data System (ADS)

A modern space mission is assembled from multiple phases or events such as impulsive maneuvers, coast arcs, thrust arcs and planetary flybys. Traditionally, a mission planner would resort to intuition and experience to develop a sequence of events for the multiphase mission and to find the space trajectory that minimizes propellant use by solving the associated continuous optimal control problem. This strategy, however, will most likely yield a sub-optimal solution, as the problem is sophisticated for several reasons. For example, the number of events in the optimal mission structure is not known a priori and the system equations of motion change depending on what event is current. In this work a framework for the automated design of multiphase space missions is presented using hybrid optimal control (HOC). The method developed uses two nested loops: an outer-loop that handles the discrete dynamics and finds the optimal mission structure in terms of the categorical variables, and an inner-loop that performs the optimization of the corresponding continuous-time dynamical system and obtains the required control history. Genetic algorithms (GA) and direct transcription with nonlinear programming (NLP) are introduced as methods of solution for the outer-loop and inner-loop problems, respectively. Automation of the inner-loop, continuous optimal control problem solver, required two new technologies. The first is a method for the automated construction of the NLP problems resulting from the use of a direct solver for systems with different structures, including different numbers of categorical events. The method assembles modules, consisting of parameters and constraints appropriate to each event, sequentially according to the given mission structure. The other new technology is for a robust initial guess generator required by the inner-loop NLP problem solver. Two new methods were developed for cases including low-thrust trajectories. The first method, based on GA, approximates optimal control histories by incorporating boundary conditions explicitly using a conditional penalty function. The second method, feasible region analysis, is based on GA and NLP; the GA approximates the optimal boundary points of low-thrust arcs while NLP finds the required control histories. The solution of two representative multiphase space mission design problems shows the effectiveness of the methods developed.

Chilan, Christian Miguel

236

Design of Optimal Systolic Algorithms for the Transitive Closure Problem

New optimal systolic algorithms for the transitive closure problem on ring and linear arrays of processors is presented. The data dependency of the Warshal-Floyd algorithm is exploited to obtain highly pipelined parallel algorithms. One of the algorithms is asymptotically seven times more cost-effective than previous algorithms for computing transitive closure problems. The authors introduce a new expository device, called the

Dilip Sarkar; Amar Mukherjee

1992-01-01

237

Optimal Approximation Algorithms for Digital Filter Design.

NASA Astrophysics Data System (ADS)

Several new algorithms are presented for the optimal approximation and design of various classes of digital filters. An iterative algorithm is developed for the efficient design of unconstrained and constrained infinite impulse response (IIR) digital filters. Both in the unconstrained and constrained cases, the numerator and denominator of the filter transfer function are designed iteratively by recourse to the Remez algorithm and to appropriate design parameters and criteria, at each iteration. This makes it possible for the algorithm to be implemented by means of a short main program which uses (at each iteration) the linear phase FIR filter design algorithm of McClellan et al. as a subroutine. The approach taken also permits the filter to be designed with a desired ripple ratio. Also, the algorithm determines automatically the minimum passband ripple corresponding to the prescribed orders and band edges of the filter. The filter is designed directly without guessing the passband ripple or stopband ripple. Another algorithm, based on similar principles, is developed for the design of a nonlinear phase finite impulse response (FIR) filter, whose transfer function optimally approximates a desired magnitude response, there being no constraints imposed on the phase response. A similar algorithm is presented for the design of two new classes of FIR digital filters, one linear phase and the other nonlinear phase. A filter of either class has significantly reduced number of multiplications compared to the one obtained by its conventional counterpart, with respect to a given frequency response. In the case of linear phase, by introducing the new class of digital filters into the design of multistage decimators and interpolators for narrow-band filter implementation, it is found that an efficient narrow-band filter requiring considerably lower multiplication rate than the conventional linear phase FIR design can be obtained. The amount of data storage required by the new class of nonlinear phase FIR filters is significantly less than its linear phase counterpart. Finally, the design of a (finite-impulse-response) FIR digital filter with some of the coefficients constrained to zero is formulated as a linear programming (LP) problem and the LP technique is then used to design this class of constrained FIR digital filters. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI.

Liang, Junn-Kuen

238

An experimental study of hybridizing cultural algorithms and local search.

In this paper the performance of the Cultural Algorithms-Iterated Local Search (CA-ILS), a new continuous optimization algorithm, is empirically studied on multimodal test functions proposed in the Special Session on Real-Parameter Optimization of the 2005 Congress on Evolutionary Computation. It is compared with state-of-the-art methods attending the Session to find out whether the algorithm is effective in solving difficult problems. The test results show that CA-ILS may be a competitive method, at least in the tested problems. The results also reveal the classes of problems where CA-ILS can work well and/or not well. PMID:18344219

Nguyen, Trung Thanh; Yao, Xin

2008-02-01

239

Optimally Controlling Hybrid Electric Vehicles using Path Forecasting

The paper examines path-dependent control of Hybrid Electric Vehicles (HEVs). In this approach we seek to improve HEV fuel economy by optimizing charging and discharging of the vehicle battery depending on the forecasted ...

Kolmanovsky, Ilya V.

240

Two Hybrid Algorithms for Multiple Sequence Alignment

NASA Astrophysics Data System (ADS)

In order to design life saving drugs, such as cancer drugs, the design of Protein or DNA structures has to be accurate. These structures depend on Multiple Sequence Alignment (MSA). MSA is used to find the accurate structure of Protein and DNA sequences from existing approximately correct sequences. To overcome the overly greedy nature of the well known global progressive alignment method for multiple sequence alignment, we have proposed two different algorithms in this paper; one is using an iterative approach with a progressive alignment method (PAMIM) and the second one is using a genetic algorithm with a progressive alignment method (PAMGA). Both of our methods started with a "kmer" distance table to generate single guide-tree. In the iterative approach, we have introduced two new techniques: the first technique is to generate Guide-trees with randomly selected sequences and the second is of shuffling the sequences inside that tree. The output of the tree is a multiple sequence alignment which has been evaluated by the Sum of Pairs Method (SPM) considering the real value data from PAM250. In our second GA approach, these two techniques are used to generate an initial population and also two different approaches of genetic operators are implemented in crossovers and mutation. To test the performance of our two algorithms, we have compared these with the existing well known methods: T-Coffee, MUSCEL, MAFFT and Probcon, using BAliBase benchmarks. The experimental results show that the first algorithm works well for some situations, where other existing methods face difficulties in obtaining better solutions. The proposed second method works well compared to the existing methods for all situations and it shows better performance over the first one.

Naznin, Farhana; Sarker, Ruhul; Essam, Daryl

2010-01-01

241

Intervals in evolutionary algorithms for global optimization

Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.

Patil, R.B.

1995-05-01

242

A modified hybrid particle swarm optimization approach for unit commitment

This paper presents a new solution to thermal unit- commitment (UC) problem based on a modified hybrid particle swarm optimization (MHPSO). Hybrid real and binary PSO is coupled with the proposed heuristic based constraint satisfaction strategy that makes the solutions\\/particles feasible for PSO. The velocity equation of particle is also modified to prevent particle stagnation. Unit commitment priority is used

Le Thanh Xuan Yen; Deepak Sharma; Dipti Srinivasan; Pindoriya Naran Manji

2011-01-01

243

Optimal Scheduling for Energy Harvesting Transmitters with Hybrid Energy Storage

Optimal Scheduling for Energy Harvesting Transmitters with Hybrid Energy Storage Omur Ozel Khurram with an energy harvesting transmitter which has a hybrid energy storage unit composed of a perfectly efficient super-capacitor (SC) and an inefficient battery. The SC has finite space for energy storage while

Ulukus, Sennur

244

A numerical study of hybrid optimization methods for the molecular conformation problems

An important area of research in computational biochemistry is the design of molecules for specific applications. The design of these molecules depends on the accurate determination of their three-dimensional structure or conformation. Under the assumption that molecules will settle into a configuration for which their energy is at a minimum, this design problem can be formulated as a global optimization problem. The solution of the molecular conformation problem can then be obtained, at least in principle, through any number of optimization algorithms. Unfortunately, it can easily be shown that there exist a large number of local minima for most molecules which makes this an extremely difficult problem for any standard optimization method. In this study, we present results for various optimization algorithms applied to a molecular conformation problem. We include results for genetic algorithms, simulated annealing, direct search methods, and several gradient methods. The major result of this study is that none of these standard methods can be used in isolation to efficiently generate minimum energy configurations. We propose instead several hybrid methods that combine properties of several local optimization algorithms. These hybrid methods have yielded better results on representative test problems than single methods.

Meza, J.C.; Martinez, M.L.

1993-05-01

245

NASA Astrophysics Data System (ADS)

Many evolutionary computation methods have been proposed and applied to real world problems. But gradient methods are still effective in problems involving real-coded parameters. In addition, it is desirable to find not only an optimal solution but also plural optimal and semi-optimal solutions in most real world problems. Although a hybrid algorithm combining Immune Algorithm (IA) and Quasi-Newton method (QN) has been proposed for multiple solution search, its memory cell control sometimes fails to keep semi-optimal solutions whose evaluation value is not so high. In addition, because the hybrid algorithm applies QN only to memory cell candidates, QN can be used as local search operator only after global search by IA. This paper proposes an improved memory cell control which restricts existence of redundant memory cells, and a QN application method which uses QN even in early search stage. Experimental results have shown that the hybrid algorithm involving the proposed improvements can find optimal and semi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal functions involving epistasis.

Hirotani, Yusuke; Ono, Satoshi; Nakayama, Shigeru

246

Registration of range data using a hybrid simulated annealing and iterative closest point algorithm

The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.

LUCK,JASON; LITTLE,CHARLES Q.; HOFF,WILLIAM

2000-04-17

247

Urban drain layout optimization using PBIL algorithm

NASA Astrophysics Data System (ADS)

Strengthen the environmental protection is one of the basic national policies in China. The optimization of urban drain layout plays an important role to the protection of water ecosystem and urban environment. The paper puts forward a method to properly locate urban drain using population based incremental learning (PBIL) algorithm. The main factors such as regional containing sewage capacity, sewage disposal capacity quantity limit of drains within specific area are considered as constraint conditions. Analytic hierarchy process is used to obtain weight of each factor, and spatial analysis of environmental influencing factors is carried on Based on GIS. Penalty function method is put forward to model the problem and object function is to guarantee economy benefit. The algorithm is applied to the drain layout engineering of Nansha District, Guangzhou City, China. The drain layout obtained though PBIL algorithm excels traditional method and it can protect the urban environment more efficiently and ensure the healthy development of water ecosystem more successfully. The result has also proved that PBIL algorithm is a good method in solving this question because of its robust performance and stability which supplied strong technologic support to the sustainable development of environment.

Wan, Shanshan; Hao, Ying; Qiu, Dongwei; Zhao, Xu

2008-10-01

248

Optimizing coherent anti-Stokes Raman scattering by genetic algorithm controlled pulse shaping

NASA Astrophysics Data System (ADS)

The hybrid coherent anti-Stokes Raman scattering (CARS) has been successful applied to fast chemical sensitive detections. As the development of femto-second pulse shaping techniques, it is of great interest to find the optimum pulse shapes for CARS. The optimum pulse shapes should minimize the non-resonant four wave mixing (NRFWM) background and maximize the CARS signal. A genetic algorithm (GA) is developed to make a heuristic searching for optimized pulse shapes, which give the best signal the background ratio. The GA is shown to be able to rediscover the hybrid CARS scheme and find optimized pulse shapes for customized applications by itself.

Yang, Wenlong; Sokolov, Alexei

2010-10-01

249

Global optimization algorithm for heat exchanger networks

This paper deals with the global optimization of heat exchanger networks with fixed topology. It is shown that if linear area cost functions are assumed, as well as arithmetic mean driving force temperature differences in networks with isothermal mixing, the corresponding nonlinear programming (NLP) optimization problem involves linear constraints and a sum of linear fractional functions in the objective which are nonconvex. A rigorous algorithm is proposed that is based on a convex NLP underestimator that involves linear and nonlinear estimators for fractional and bilinear terms which provide a tight lower bound to the global optimum. This NLP problem is used within a spatial branch and bound method for which branching rules are given. Basic properties of the proposed method are presented, and its application is illustrated with several example problems. The results show that the proposed method only requires few nodes in the branch and bound search.

Quesada, I.; Grossmann, I.E. (Carnegie Mellon Univ., Pittsburgh, PA (United States))

1993-03-01

250

Multiple Birth and Cut Algorithm for Point Process Optimization

Multiple Birth and Cut Algorithm for Point Process Optimization Ahmed Gamal-Eldin, Xavier Descombes, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC

251

Optimal control of trading algorithms: a general impulse control approach

inclusion of a new effect in the "optimal-control-oriented" original framework gave birth to a specificOptimal control of trading algorithms: a general impulse control approach Bruno Bouchard , Ngoc-day trading based on the control of trading algorithms. Given a generic parameterized algorithm, we control

Paris-Sud XI, Université de

252

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects C. Yal#24;c#16;n Kaya School@maths.uwa.edu.au Abstract The Leap-Frog Algorithm was originally devised to #12;nd geodesics in connected complete with generalizing the mathematical rigour of the leap-frog algorithm to a class of optimal control problems

Noakes, Lyle

253

An Iterative Global Optimization Algorithm for Potential Energy Minimization

[1]. The algorithm is tested on two different potential energy functions. The first function algorithms considering the potential functions as sample global optimization test problems. In [12An Iterative Global Optimization Algorithm for Potential Energy Minimization N. P. Moloi and M. M

254

An Iterative Global Optimization Algorithm for Potential Energy Minimization

[1]. The algorithm is tested on two different potential energy functions. The first function algorithms considering the potential functions as sample global optimization test problems. In [12 the potential functions as sample global optimization problems only for testing our algorithm. We do

255

Parallel Hybrid Vehicle Optimal Storage System

NASA Technical Reports Server (NTRS)

A paper reports the results of a Hybrid Diesel Vehicle Project focused on a parallel hybrid configuration suitable for diesel-powered, medium-sized, commercial vehicles commonly used for parcel delivery and shuttle buses, as the missions of these types of vehicles require frequent stops. During these stops, electric hybridization can effectively recover the vehicle's kinetic energy during the deceleration, store it onboard, and then use that energy to assist in the subsequent acceleration.

Bloomfield, Aaron P.

2009-01-01

256

ISI Effects in a Hybrid ICA-SVM Modulation Recognition Algorithm

ISI Effects in a Hybrid ICA-SVM Modulation Recognition Algorithm David Boutte and Balu Santhanam proposed hybrid ICA-SVM modulation recogni- tion algorithm is studied. The algorithm combines elements of cyclo-spectral analysis, ICA and SVM algorithms to distinguish between different types of continuous

Santhanam, Balu

257

Lunar Habitat Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.

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

2007-01-01

258

The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148

Ahmed, Zakir Hussain

2014-01-01

259

Instrument design and optimization using genetic algorithms

This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.

Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter [Institut Laue-Langevin, BP 156, F-38042 Grenoble Cedex 9 (France); Hahn-Meitner Institut, Glienicker Strasse 100, D-14109 Berlin (Germany); Institut Laue-Langevin, BP 156, F-38042, Grenoble Cedex 9 (France)

2006-10-15

260

Hybrid regularization image restoration algorithm based on total variation

NASA Astrophysics Data System (ADS)

To reduce the noise amplification and ripple phenomenon in the restoration result by using the traditional Richardson-Lucy deconvolution method, a novel hybrid regularization image restoration algorithm based on total variation is proposed in this paper. The key ides is that the hybrid regularization terms are employed according to the characteristics of different regions in the image itself. At the same time, the threshold between the different regularization terms is selected according to the golden section point which takes into account the human eye's visual feeling. Experimental results show that the restoration results of the proposed method are better than that of the total variation Richardson-Lucy algorithm both in PSNR and MSE, and it has the better visual effect simultaneously.

Zhang, Hongmin; Wang, Yan

2013-09-01

261

Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members

Shan He; Q. Henry Wu; J. R. Saunders

2009-01-01

262

Optimization of the fuel economy of a hybrid electric vehicle

In this paper, we present a simulation study of the potential improvement in fuel economy that is available by optimizing the usage of the high voltage battery in a hybrid electric vehicle with the configuration of the Toyota Prius. A mathematical model of the vehicle system is proposed, and used with an optimizer to find the battery usage over a

J. G. Supina; S. Awad

2003-01-01

263

Testing trivializing maps in the Hybrid Monte Carlo algorithm

We test a recent proposal to use approximate trivializing maps in a field theory to speed up Hybrid Monte Carlo simulations. Simulating the CPN?1 model, we find a small improvement with the leading order transformation, which is however compensated by the additional computational overhead. The scaling of the algorithm towards the continuum is not changed. In particular, the effect of the topological modes on the autocorrelation times is studied. PMID:21969733

Engel, Georg P.; Schaefer, Stefan

2011-01-01

264

A cross-layer optimization algorithm for wireless sensor network

NASA Astrophysics Data System (ADS)

Energy is critical for typical wireless sensor networks (WSN) and how to energy consumption and maximize network lifetime are big challenges for Wireless sensor networks; cross layer algorithm is main method to solve this problem. In this paper, firstly, we analyze current layer-based optimal methods in wireless sensor network and summarize the physical, link and routing optimization techniques. Secondly we compare some strategies in cross-layer optimization algorithms. According to the analysis and summary of the current lifetime algorithms in wireless sensor network A cross layer optimization algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional Leach routing protocol. Simulation results show that this algorithm is an excellent cross layer algorithm for reducing energy consumption.

Wang, Yan; Liu, Le Qing

2010-07-01

265

Local optimality of dictionary learning algorithms Boris Mailh

Local optimality of dictionary learning algorithms Boris MailhÃ© Centre for Digital Music School dictionary learning algorithms. We focus on three algorithms: the Olshausen and Field algorithm (Ols-DLA) [1 matrix of training data. We consider the following dictionary learning problem min ,X S - X 2 2 (1

Plumbley, Mark

266

Study on Water Pollution Diffusion by Artificial Immunity Optimization Algorithm

Artificial immune algorithm is a new bionic algorithms, it becomes a hot spot. Artificial immune algorithm has self-adjustment ability and adaptive capacity of the environment and can deal with complex optimization problems in parallel. Immune algorithm takes concentration and affinity as standards. Thus low concentration, high-fit individuals have more breeding opportunities. As attention to the diversity of individuals of solution

Li Yu; Jia-quan Wang

2010-01-01

267

Hybrid least-squares algorithms for approximate policy evaluation

The goal of approximate policy evaluation is to “best” represent a target value function according to a specific criterion.\\u000a Different algorithms offer different choices of the optimization criterion. Two popular least-squares algorithms for performing\\u000a this task are the Bellman residual method, which minimizes the Bellman residual, and the fixed point method, which minimizes the projection of the Bellman residual. When

Jeff Johns; Marek Petrik; Sridhar Mahadevan

2009-01-01

268

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

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

2014-01-01

269

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

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

2014-01-01

270

A Distributed Particle Swarm Optimization Algorithm for Swarm Robotic Applications

We have derived a version of the particle swarm optimization algorithm that is suitable for a swarm consisting of a large number of small, mobile robots. The algorithm, called the distributed PSO (dPSO), is for \\

James M. Hereford

2006-01-01

271

Stochastic Optimal Control for Series Hybrid Electric Vehicles

Increasing demand for improving fuel economy and reducing emissions has stimulated significant research and investment in hybrid propulsion systems. In this paper, we address the problem of optimizing online the supervisory control in a series hybrid configuration by modeling its operation as a controlled Markov chain using the average cost criterion. We treat the stochastic optimal control problem as a dual constrained optimization problem. We show that the control policy that yields higher probability distribution to the states with low cost and lower probability distribution to the states with high cost is an optimal control policy, defined as an equilibrium control policy. We demonstrate the effectiveness of the efficiency of the proposed controller in a series hybrid configuration and compare it with a thermostat-type controller.

Malikopoulos, Andreas [ORNL] [ORNL

2013-01-01

272

Global Optimization of Chemical Processes using Stochastic Algorithms

of a fermentation process, to deterÂ mine multiphase equilibria, for the optimal control of a penicillin reactor of the penicillin reactor and the nonÂdifferentiable system. 1. INTRODUCTION GradientÂbased optimization algorithms

Neumaier, Arnold

273

Adaptive hybrid optimal quantum control for imprecisely characterized systems

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the its input variables, the quantum system's parameters. We show how to overcome this by Adaptive Hybrid Optimal Control (Ad-HOC). This protocol combines open- and closed-loop optimal by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity measure with a gradient-free method. For typical settings in solid-state quantum information processing, Ad-Hoc enhances gate fidelities by an order of magnitude hence making optimal control theory applicable and useful.

D. J. Egger; F. K. Wilhelm

2014-06-24

274

Hybrid optimization for 13C metabolic flux analysis using systems parametrized by compactification

Background The importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary. Results For flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori. Conclusion This fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology. PMID:18366780

Yang, Tae Hoon; Frick, Oliver; Heinzle, Elmar

2008-01-01

275

A parallel textured algorithm for optimal power flow

Abur (Member) ostas N. Georglna&les (Member) )of Mo'pgan , ' (Member) Jo W. Howze (Head of Department) August 1992 ABSTRACT A Parallel Textured Algorithm for Optimal Power I'low Analysis. (August 1992) Shih-Chieh Hsieh, B. S. , National.... The Optimal Power Flow Problems B. Present Status of Optimal Power Flow Analysis C. Solution Method D. Thesis Structure . FORMULATION OF OPTIMAL POWER FLOW ANALYSIS . 6 A. Conventional Formulation of Optimal Power Analysis B. Reformulation of Optimal...

Hsieh, Shih-Chieh

1992-01-01

276

PDE Nozzle Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.

Billings, Dana; Turner, James E. (Technical Monitor)

2000-01-01

277

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for ...

followed by an interior-point method for the classical SVM in the second phase. Both SVM ... mainly because the size of the optimization problem is very large. ... algorithm can solve large classification problems efficiently and accurately. As we ..... which can be represented by the set of linear constraints in v. DT v ? w = 0, ...

2014-07-19

278

Hybrid NN/SVM Computational System for Optimizing Designs

NASA Technical Reports Server (NTRS)

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan

2009-01-01

279

Efficiencies and optimization of HMC algorithms in pure gauge theory

As a prerequisite to dynamical fermion simulations a detailed study of optimal parameters and scaling behavior is conducted for the quenched Schr\\"odinger functional at fixed renormalized coupling. We compare standard hybrid overrelaxation techniques with local and global hybrid Monte Carlo. Our efficiency measure is designed to be directly relevant for the strong coupling constant as used by the ALPHA collaboration.

Bernd Gehrmann; Ulli Wolff

1999-08-04

280

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

NASA Astrophysics Data System (ADS)

Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

Jolai, Fariborz; Assadipour, Ghazal

281

Speed and convergence properties of gradient algorithms for optimization of IMRT.

Gradient algorithms are the most commonly employed search methods in the routine optimization of IMRT plans. It is well known that local minima can exist for dose-volume-based and biology-based objective functions. The purpose of this paper is to compare the relative speed of different gradient algorithms, to investigate the strategies for accelerating the optimization process, to assess the validity of these strategies, and to study the convergence properties of these algorithms for dose-volume and biological objective functions. With these aims in mind, we implemented Newton's, conjugate gradient (CG), and the steepest decent (SD) algorithms for dose-volume- and EUD-based objective functions. Our implementation of Newton's algorithm approximates the second derivative matrix (Hessian) by its diagonal. The standard SD algorithm and the CG algorithm with "line minimization" were also implemented. In addition, we investigated the use of a variation of the CG algorithm, called the "scaled conjugate gradient" (SCG) algorithm. To accelerate the optimization process, we investigated the validity of the use of a "hybrid optimization" strategy, in which approximations to calculated dose distributions are used during most of the iterations. Published studies have indicated that getting trapped in local minima is not a significant problem. To investigate this issue further, we first obtained, by trial and error, and starting with uniform intensity distributions, the parameters of the dose-volume- or EUD-based objective functions which produced IMRT plans that satisfied the clinical requirements. Using the resulting optimized intensity distributions as the initial guess, we investigated the possibility of getting trapped in a local minimum. For most of the results presented, we used a lung cancer case. To illustrate the generality of our methods, the results for a prostate case are also presented. For both dose-volume and EUD based objective functions, Newton's method far outperforms other algorithms in terms of speed. The SCG algorithm, which avoids expensive "line minimization," can speed up the standard CG algorithm by at least a factor of 2. For the same initial conditions, all algorithms converge essentially to the same plan. However, we demonstrate that for any of the algorithms studied, starting with previously optimized intensity distributions as the initial guess but for different objective function parameters, the solution frequently gets trapped in local minima. We found that the initial intensity distribution obtained from IMRT optimization utilizing objective function parameters, which favor a specific anatomic structure, would lead to a local minimum corresponding to that structure. Our results indicate that from among the gradient algorithms tested, Newton's method appears to be the fastest by far. Different gradient algorithms have the same convergence properties for dose-volume- and EUD-based objective functions. The hybrid dose calculation strategy is valid and can significantly accelerate the optimization process. The degree of acceleration achieved depends on the type of optimization problem being addressed (e.g., IMRT optimization, intensity modulated beam configuration optimization, or objective function parameter optimization). Under special conditions, gradient algorithms will get trapped in local minima, and reoptimization, starting with the results of previous optimization, will lead to solutions that are generally not significantly different from the local minimum. PMID:15191303

Zhang, Xiaodong; Liu, Helen; Wang, Xiaochun; Dong, Lei; Wu, Qiuwen; Mohan, Radhe

2004-05-01

282

An application of genetic algorithm to DNA sequencing by oligonucleotide hybridization

The authors propose a sequencing algorithm for oligonucleotide hybridization using the genetic algorithm. The target DNA sequence reconstructed by the hybridization method is relatively long for genetic algorithm (GA), so special setups of the genetic operation are necessary. The authors introduce the grouping GA and a special crossover method for this problem. They carried out some experiments of sequence reconstruction,

H. Douzono; S. Hara; Y. Noguchi

1998-01-01

283

A robust unit commitment algorithm for hydro-thermal optimization

This paper presents a unit commitment algorithm which combines the Lagrangian relaxation (LR), sequential unit commitment (SUC), and optimal unit decommitment (UD) methods to solve a general hydro-thermal optimization (HTO) problem. We argue that this approach retains the advantages of the LR method while addressing the method's observed weaknesses to improve overall algorithm performance and quality of solution. The proposed

Chaa-An Li; Raymond B. Johnson; Alva J. Svoboda; Chung-Li Tseng; Eric Hsu

1998-01-01

284

A robust unit commitment algorithm for hydro-thermal optimization

This paper presents a unit commitment algorithm which combines the Lagrangian relaxation (LR), sequential unit commitment (SUC), and optimal unit decommitment (UD) methods to solve a general hydro-thermal optimization (HTO) problem. The authors argue that this approach retains the advantages of the LR method while addressing the method's observed weaknesses to improve overall algorithm performance and quality of solution. The

Chao-An Li; Raymond B. Johnson; Alva J. Svoboda; Chung-Li Tseng; Eric Hsu

1997-01-01

285

Optimized Monte Carlo Path Generation using Genetic Algorithms

In this technical report we present a new method for optimizing the generation of paths in Monte Carlo global illumination rendering algorithms. Ray tracing, particle tracing, and bidirectional ray tracing all use random walks to estimate various fluxes in the scene. The probability density functions neces- sary to generate these random walks are optimized using a genetic algorithm, such that

F. Suykens; Y. D. Willems

286

Optimal Algorithms for GSM Viterbi Modules M.Sc. Student

design of the 3rd-Generation Global System for Mobile communica- tions(GSM 3G) unit's channel codeOptimal Algorithms for GSM Viterbi Modules Kehuai Wu M.Sc. Student at Department of Informatics and Mathematical Modelling Technical University of Denmark #12;#12;Optimal Algorithms for GSM Viterbi Modules #12

287

Particle Swarm Optimization algorithm for facial emotion detection

Particle Swarm Optimization (PSO) algorithm has been applied and found to be efficient in many searching and optimization related applications. In this paper, we present a modified version of the algorithm that we successfully applied to facial emotion detection. Our approach is based on tracking the movements of facial action units (AUs) placed on the face of a subject and

Bashir Mohammed Ghandi; R. Nagarajan; Hazry Desa

2009-01-01

288

Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong

Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong Computer Science Department George Mason University Fairfax, VA 22030, USA kdejong@aic.gmu.edu Abstract Genetic Algorithms (GAs) have received a great deal of attention regarding their potential as optimization techniques for complex

George Mason University

289

Hybrid and optical implementation of the Deutsch-Jozsa algorithm

A hybrid model of the Deutsch-Jozsa algorithm is presented, inspired by the proposals of hybrid computation by S. Lloyd and P. van Loock et. al. The model is based on two observations made about both the discrete and continuous algorithms already available. First, the Fourier transform is a single-step operation in a continuous-variable (CV) setting. Additionally, any implementation of the oracle is nontrivial in both schemes. The steps of the computation are very similar to those in the CV algorithm, with the main difference being the way in which the qunats, or quantum units of analogic information, and the qubits interact in the oracle. Using both discrete and continuous states of light, linear devices, and photo-detection, an optical implementation of the oracle is proposed. For simplicity, infinitely squeezed states are used in the continuous register, whereas the optical qubit is encoded in the dual-rail logic of the KLM protocol. The initial assumption of ideal states as qunats will be dropped to study the effects of finite squeezing in the quality of the computation.

Luis A. Garcia; Jagdish R. Luthra

2009-12-31

290

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

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.

Holst, Terry L.

2004-01-01

291

Fast Algorithm To Generate Near Optimal Binary Decision Programs

NASA Astrophysics Data System (ADS)

Binary decision (BD) programs have widespread applicability in diverse areas. In many situations, problems are defined in terms of Boolean expressions, which must be converted to decision programs for evaluation. Since the construction of optimal BD programs is NP-complete, absolute optimization appears computationally intractable. This paper presents a fast heuristic algorithm for constructing near-optimal decision programs and provides an optimality metric. The algorithm involves two steps: preprocessing and optimization. The preprocessor builds a sub-optimal (in some conditions near-optimal) decision program in linear time. If sub-optimal programs are generated, the optimizer is invoked, producing a near-optimal program, using decision tables, in 0(n2) time, where n is the size of the reduced decision table generated in the first step.

Baracos, P. C.; Vroomen, L. C.; Vroomen, L. J.

1987-10-01

292

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Celik, Yuksel; Ulker, Erkan

2013-01-01

293

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Celik, Yuksel; Ulker, Erkan

2013-01-01

294

A simple elitist genetic algorithm for constrained optimization

In this paper we propose a novel approach for solving constrained optimization problems using genetic algorithms. The main emphasis of this algorithm is to be problem independent and to produce consistent results in terms of the quality of feasible solutions. The basic characteristic of this algorithm is the complete ignorance of the objective function till at least one feasible solution

Sangameswar Venkatraman; Gary G. Yen

2004-01-01

295

M-PAES: a memetic algorithm for multiobjective optimization

A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new M-PAES (memetic PAES) algorithm is carried out by testing it on a set of multiobjective 0\\/1 knapsack problems. On

Joshua D. Knowles; David W. Corne

2000-01-01

296

A real-time optimal control algorithm for greenhouse heating

A real-time control algorithm for generating optimal heating setpoints has been implemented and tested on a commercial greenhouse nursery with a tomato crop. The algorithm is based on a model of the greenhouse energy requirements and on a numerical method for optimisation, and uses weather forecasts supplied by the Meteorological Office. The algorithm resides on a PC which communicates with

Z. S. Chalabi; B. J. Bailey; D. J. Wilkinson

1996-01-01

297

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

298

A multi-sexual genetic algorithm for multiobjective optimization

In this paper a new method for solving multicriteria optimization problems by Genetic Algorithms is proposed. Standard Genetic Algorithms use a population, where each individual has the same sex (or has no sex) and any two individuals can be crossed over. In the proposed Multisexual Genetic Algorithm (MSGA), individuals have an additional feature, their sex or gender and one individual

Joanna Lis; A. E. Eiben

1997-01-01

299

An optimal online algorithm for metrical task systems

In practice, almost all dynamic systems require decisions to be made online, without full knowledge of their future impact on the system. We introduce a general model for the processing of sequences of tasks and develop a general online decision algorithm. We show that, for an important class of special cases, this algorithm is optimal among all online algorithms.Specifically, a

Allan Borodin; Nathan Linial; Michael E. Saks

1987-01-01

300

Hybrid intelligent optimization methods for engineering problems

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients

Yasin Volkan Pehlivanoglu

2010-01-01

301

A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions.

Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems. PMID:21278022

Li, Shutao; Tan, Mingkui; Tsang, Ivor W; Kwok, James Tin-Yau

2011-01-28

302

Transonic Wing Shape Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

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.

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

2002-01-01

303

Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large-scale unconstrained optimization that interlace iterations of the limited-memory BFGS method (L-BFGS) and the Hessian-free Newton method (Computat Opt Appl 2002, 21, 143-154). We test the performance of this approach as compared to those of the L-BFGS algorithm of Liu and Nocedal and the truncated Newton (TN) with automatic preconditioner of Nash, as applied to the protein bovine pancreatic trypsin inhibitor (BPTI) and a loop of the protein ribonuclease A. These systems are described by the all-atom AMBER force field with a dielectric constant epsilon = 1 and a distance-dependent dielectric function epsilon = 2r, where r is the distance between two atoms. It is shown that for the optimal parameters the hybrid approach is typically two times more efficient in terms of CPU time and function/gradient calculations than the two other methods. The advantage of the hybrid approach increases as the electrostatic interactions become stronger, that is, in going from epsilon = 2r to epsilon = 1, which leads to a more rugged and probably more nonlinear potential energy surface. However, no general rule that defines the optimal parameters has been found and their determination requires a relatively large number of trial-and-error calculations for each problem. PMID:12820130

Das, B; Meirovitch, H; Navon, I M

2003-07-30

304

Genetic-Algorithm Tool For Search And Optimization

NASA Technical Reports Server (NTRS)

SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

Wang, Lui; Bayer, Steven

1995-01-01

305

Optimal hybrid active\\/passive vibration control design

Hybrid active\\/passive control systems present unique, energy-efficient solutions to noise and vibration problems. In many applications, active systems offer the only feasible control of low-frequency, high intensity vibrations, while passive materials offer superior attenuation at higher frequencies. These two systems can be optimally coordinated for broad-band control. An energy balancing metric forms the basis of an optimization routine designed to

Jonathan D. Kemp; Robert L. Clark

2002-01-01

306

A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization

the prob- lem of solving one single-criteria convex-quadratic optimization problem by an interior-point method used for this problem. 1 Introduction Multicriteria optimization problems are a class of difficult The Interior-Point Algorithm 2.1 The Problem Let there be given a primal quadratic optimization problem (PQP

Fliege, JÃ¶rg

307

Optimal design of the magnetic microactuator using the genetic algorithm

This paper presents the optimal design of the magnetic microactuator using the genetic algorithm. The magnetic microactuator is composed of an enclosed core and a permalloy plate to form a closed magnetic circuit. The present design allows the area of the magnetic poles to be optimally enlarged and achieve a maximum force generation. To obtain the optimal geometry and maximum

C. H. Ko; J. C. Chiou

2003-01-01

308

Optimal barreling of steel shells via simulated annealing algorithm

The load carrying capacity, of externally pressurised and optimally shaped metallic shell, has been increased by 40% over the performance of an equivalent cylinder. The optimal geometry has been sought within a class of generalised ellipses by the application of simulated annealing algorithm.The optimal solution has been verified experimentally by collapsing two, nominally identical, CNC-machined, mild steel shells at about

J. B?achut

2003-01-01

309

Nonholonomic motion planning based on Newton algorithm with energy optimization

Discusses a modification of the Newton algorithm applied to nonholonomic motion planning with energy optimization. The energy optimization is performed either by optimizing motion in the space of the Jacobian matrix derived from the nonholonomic system or coupling this motion with movement toward the goal. Resulting controls are smooth and easily generated by motors or thrusters. The two methods can

Ignacy Duleba; Jurek Z. Sasiadek

2003-01-01

310

Implementation of the simultaneous perturbation algorithm for stochastic optimization

The need for solving multivariate optimization problems is pervasive in engineering and the physical and social sciences. The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for challenging optimization problems where it is difficult or impossible to directly obtain a gradient of the objective function with respect to the parameters being optimized. SPSA is based on an

JAMES C. SPALL

1998-01-01

311

A new yield optimization algorithm and its applications

The authors propose a novel yield optimization algorithm for IC design. To design a practical yield optimization system, two efforts must be made. One is to get a suitable convergence criterion, the other is to propose an efficient optimization method, by which one can reach the maximum yield point as soon as possible. A convergence criterion based on sequential tests

Zhihua Wang; Huazhong Yang; Rensheng Liu; Chongzhi Fan

1991-01-01

312

This article presents a hybrid neural network, desirability function, and genetic algorithm (NN-DF-GA) approach for optimal selection of the input process parameters for optimizing the multiresponse parameters of the electrojet drilling (EJD) process. EJD is a promising nontraditional machining technique that is used for machining microholes (<1 mm in diameter) in difficult-to-machine materials. The proposed approach first uses a back propagation

Mohan Sen; H. S. Shan

2006-01-01

313

Optimal Design of a Hybrid Electric Car with Solar Cells

A model for the optimal design of a solar hybrid vehicle is presented. The model can describe the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs for different sites. It is shown that significant fuel savings can be achieved for intermittent use with limited average power, and

I. Arsie; M. Marotta; C. Pianese; G. Rizzo; M. Sorrentino

314

Topology Optimization for Hybrid Electric Vehicles With Automated Transmissions

Currently, many different topologies are designed with different transmission technologies such as automated manual transmission (AMT) and continuously variable transmission (CVT). The choice of topology determines the energy-flow efficiency between the hybrid system, the engine, and the vehicle wheels. The optimal topology minimizing fuel consumption is influenced by the transmission technology. Therefore, an AMT (high efficiency) and a push-belt CVT

Theo Hofman; Søren Ebbesen; Lino Guzzella

2012-01-01

315

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

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.

Holst, Terry L.

2005-01-01

316

Lazy skip-lists: An algorithm for fast hybridization-expansion quantum Monte Carlo

NASA Astrophysics Data System (ADS)

The solution of a generalized impurity model lies at the heart of electronic structure calculations with dynamical mean field theory. In the strongly correlated regime, the method of choice for solving the impurity model is the hybridization-expansion continuous-time quantum Monte Carlo (CT-HYB). Enhancements to the CT-HYB algorithm are critical for bringing new physical regimes within reach of current computational power. Taking advantage of the fact that the bottleneck in the algorithm is a product of hundreds of matrices, we present optimizations based on the introduction and combination of two concepts of more general applicability: (a) skip lists and (b) fast rejection of proposed configurations based on matrix bounds. Considering two very different test cases with d electrons, we find speedups of ˜25 up to ˜500 compared to the direct evaluation of the matrix product. Even larger speedups are likely with f electron systems and with clusters of correlated atoms.

Sémon, P.; Yee, Chuck-Hou; Haule, Kristjan; Tremblay, A.-M. S.

2014-08-01

317

Solving the vehicle routing problem by a hybrid meta-heuristic algorithm

NASA Astrophysics Data System (ADS)

The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the total distance traveled by all the vehicles. This paper presents a hybrid two-phase algorithm called sweep algorithm (SW) + ant colony system (ACS) for the classical VRP. At the first stage, the VRP is solved by the SW, and at the second stage, the ACS and 3-opt local search are used for improving the solutions. Extensive computational tests on standard instances from the literature confirm the effectiveness of the presented approach.

Yousefikhoshbakht, Majid; Khorram, Esmaile

2012-08-01

318

A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms

NASA Astrophysics Data System (ADS)

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.

Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.

319

Design Optimization of an Axial Fan Blade Through Multi-Objective Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ? -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.

Kim, Jin-Hyuk; Choi, Jae-Ho; Husain, Afzal; Kim, Kwang-Yong

2010-06-01

320

NASA Astrophysics Data System (ADS)

Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.

Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei

2014-04-01

321

Biology-Derived Algorithms in Engineering Optimization

Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.

Yang, Xin-She

2010-01-01

322

Evaluation of a particle swarm algorithm for biomechanical optimization.

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm's global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms--a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units. PMID:16060353

Schutte, Jaco F; Koh, Byung-Il; Reinbolt, Jeffrey A; Haftka, Raphael T; George, Alan D; Fregly, Benjamin J

2005-06-01

323

An Adaptive Unified Differential Evolution Algorithm for Global Optimization

In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.

Qiang, Ji; Mitchell, Chad

2014-11-03

324

Genetic algorithms for multicriteria shape optimization of induction furnace

NASA Astrophysics Data System (ADS)

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

K?s, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

2012-09-01

325

Optimal design of automotive hybrid powertrain systems

Alternative powertrains for automotive applications aim at improving emissions and fuel economy. Lack of experience with these relatively new technologies makes them ideal applications for computer-based modeling and simulation studies. There is a variety of configurations, control strategies, and design variable choices that can be made. If mathematical models exist, rigorous optimization techniques can be used to explore the design

Ryan Fellini; Nestor Michelena; Panos Papalambros; Michael Sasena

1999-01-01

326

The fastest bowlers and pitchers, who are able to perform at levels that exceed those of their team-mates, should have a combination of optimal physical characteristics and techniques. The relationship between ball speed and anthropometrical characteristics of 48 professional baseball pitchers was investigated using a hybrid evolutionary algorithm (HEA). The average height and weight of the subjects were 191.4±4.7 cm

L. Valandro; L. Colombo; H. Cao; F. Recknagel; S. Dun

327

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

NASA Technical Reports Server (NTRS)

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.

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

2005-01-01

328

A hybrid search algorithm for swarm robots searching in an unknown environment.

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

329

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

330

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

331

Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, GeneticAlgorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially for

Michael Affenzeller

2001-01-01

332

Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved e-ciently only by heuristics. Both, Genetic Algorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially

Michael Afienzeller

2001-01-01

333

A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows

A variety of hybrid genetic algorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of the intermediate solutions computed during the evolutionary process to explore the solution space. This paper presents a new hybrid genetic algorithm

Jean Berger; Martin Salois; Regent Begin

1998-01-01

334

Segmentation of MR and CT Images Using a Hybrid Neural Network Trained by Genetic Algorithms

A novel hybrid neural network trained by the genetic algorithms is presented. Genetic algorithms are used to improve the neural net's classification performance while minimizing the number of nodes. Each node of the network forms a closed region in the input space. The closed regions, which are formed by the nodes, intersect each other. The performance of the proposed hybrid

Zümray Dokur

2002-01-01

335

Reliability-Based Optimization Using Evolutionary Algorithms

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist ...

Deb, Kalyanmoy

336

Bio-inspired optimization algorithms for smart antennas

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external ...

Zuniga, Virgilio

2011-11-22

337

A Quasi-Newton Algorithm for Nonconvex, Nonsmooth Optimization ...

May 26, 2014 ... value optimization [1], compressed sensing [8, 9, 16], and .... the term “Hessian” loosely as a matrix that approximates changes in ?f about ...... In this section, we prove that Algorithm 1 is globally convergent from remote.

2014-05-26

338

Application of a gradient-based algorithm to structural optimization

Optimization methods have shown to be efficient at improving structural design, but their use is limited in the engineering practice by the difficulty of adapting state-of-the-art algorithms to particular engineering ...

Ghisbain, Pierre

2009-01-01

339

Greedy Algorithms for Optimized DNA Sequencing Allon G. Percus

Greedy Algorithms for Optimized DNA Sequencing Allon G. Percus David C. Torney Abstract We discussÂB258, Los Alamos National Laboratory, Los Alamos, NM 87545. E-mail: percus@lanl.gov. TÂ10 and Center

Percus, Allon

340

PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm

Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198

Lim, Wei Chen Esmonde; Kanagaraj, G.; Ponnambalam, S. G.

2014-01-01

341

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with Decoupled-7501 Patrick Xavier Sandia National Laboratories, Albuquerque NM 87185-0951 Keywords: robot motion planning, kinodynamics, polyhedral obstacles Abstract: We consider the following problem: given a robot system, nd

Richardson, David

342

A Hybrid Method for Optimization (Discrete PSO + CLA)

Abstract-PSO is an evolutionary algorithm that is inspired from collective behavior of animals such as fish schooling or bird flocking. One of the drawbacks of this model is premature convergence and trapping in local optima. In this paper we propose a solution to this problem in discrete version of PSO that uses Learning Automata and introduce a Cellular Learning Automata (CLA) based discrete PSO. Experimental results on five optimization problems show the superiority of the proposed algorithm.

B. Jafarpour; M. R. Meybodi; S. Shiry

343

Genetic algorithms - What fitness scaling is optimal?

NASA Technical Reports Server (NTRS)

A problem of choosing the best scaling function as a mathematical optimization problem is formulated and solved under different optimality criteria. A list of functions which are optimal under different criteria is presented which includes both the best functions empirically proved and new functions that may be worth trying.

Kreinovich, Vladik; Quintana, Chris; Fuentes, Olac

1993-01-01

344

Branch-and-Lift Algorithm for Global Optimal Control - Optimization ...

example in controlling a car or a robot in the presence of obstacles, a local solver will typically fail to determine .... regardless of the fact that resistances, diodes or other electric devices with nonlinear characteristics may be ...... hybrid methods.

2013-07-23

345

A Unified Differential Evolution Algorithm for Global Optimization

Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.

Qiang, Ji; Mitchell, Chad

2014-06-24

346

Cellular Probabilistic Evolutionary Algorithms for Real-Coded Function Optimization

NASA Astrophysics Data System (ADS)

We propose a novel Cellular Probabilistic Evolutionary Algorithm (CPEA) based on a probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. Furthermore, the cellular structure of the proposed algorithm aims to provide an appropriate tradeoff between exploitation and exploration. Experimental results show that the performance of CPEA in several numerical benchmark problems is improved when compared with other evolutionary algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA).

Akbarzadeh T., M. R.; Tayarani N., M.

347

An Optimal Minimum Spanning Tree Algorithm

We establish that the algorithmic complexity of the minimum spanning tree problem isequal to its decision-tree complexity. Specifically, we present a deterministic algorithm to find aminimum spanning forest of a graph with n vertices and m edges that runs in time O(T(m; n))where Tis the minimum number of edge-weight comparisons needed to determine the solution.The algorithm is quite simple and

Seth Pettie; Vijaya Ramachandran

2000-01-01

348

Superscattering of light optimized by a genetic algorithm

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.

Mirzaei, Ali, E-mail: ali.mirzaei@anu.edu.au; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S. [Nonlinear Physics Center, Research School of Physics and Engineering, Australian National University, Canberra ACT 0200 (Australia)

2014-07-07

349

An algorithm for solving control constrained optimal control problems

An algorithm, with an approach similar to the Han-Powell method in finite-dimensional optimization, is devised to solve continuous-time optimal control problems where the control variables are constrained. The algorithm is based on a second-order approximation to the change of the cost functional due to a change in the control. Further approximation of that summation produces a simple convex functional. It

Baoming Ma; W. S. Levine

1993-01-01

350

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms

HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE May 2010 Major Subject: Petroleum Engineering HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate...

Gibbs, Trevor Howard

2011-08-08

351

Optimization of image processing algorithms on mobile platforms

This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time

Pramod Poudel; Mukul Shirvaikar

2011-01-01

352

A study of speech emotion recognition based on hybrid algorithm

NASA Astrophysics Data System (ADS)

To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

2011-10-01

353

Double Motor Coordinated Control Based on Hybrid Genetic Algorithm and CMAC

NASA Astrophysics Data System (ADS)

A novel hybrid cerebellar model articulation controller (CMAC) and online adaptive genetic algorithm (GA) controller is introduced to control two Brushless DC motor (BLDCM) which applied in a biped robot. Genetic Algorithm simulates the random learning among the individuals of a group, and CMAC simulates the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments have been done in MATLAB/SIMULINK. Analysis among GA, hybrid GA-CMAC and CMAC feed-forward control is also given. The results prove that the torque ripple of the coordinated control system is eliminated by using the hybrid GA-CMAC algorithm.

Cao, Shaozhong; Tu, Ji

354

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (?). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

355

-based methods are, in general, much more cost effective than algorithms that require only the function valuesMethods for optimizing large molecules Part III.y An improved algorithm for geometry optimization structure), (b) oscillation around an inflection point on the potential energy surface, (c) numerical

Schlegel, H. Bernhard

356

Genetic algorithm optimization applied to electromagnetics: a review

Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are

Daniel S. Weile; Eric Michielssen

1997-01-01

357

An optimal scheduling algorithm for electronic control unit in vehicles

In this paper, we propose an optimal scheduling algorithm for electronic control units in vehicles which usually connect with low speed CAN bus or LIN bus and apply to automotive body control, such as lamp control, power window control, windscreen wiper control. This algorithm is used to (1) guarantee schedulability of mandatory parts of periodic tasks, (2) guarantee aperiodic tasks

Yuan Sun; Xiaobing Zhao; Guosheng Yang

2009-01-01

358

Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics

: the reconstruction of evolutionary histories (phylogenies) from molecular data such as DNA sequences. OurReconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics Bernard M: we conducted an exÂ tensive study of quartetÂbased reconstruction algorithms within a parameter

Moret, Bernard

359

MOMMIE Knows Best: Systematic Optimizations for Verifiable Distributed Algorithms

Introduction Complex distributed algorithms become running systems through an integration with optimizations that target the system's deployment environment. Although expedient, this approach has disadvantages. First as the fundamental cause for this problem. On one hand, algorithm designers need abstraction to simplify the job

Maniatis, Petros

360

MOGA algorithm for multi-objective optimization of aircraft detection

This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied to a multi-objective optimization of aircraft, measure of fitness degree was discussed as an emphasis. The solutions were analyzed and compares with original BP neural networks algorithm, which is better than the network trained

Hongguang Sun; Yuxue Pan; Jingbo Zhang

2006-01-01

361

AN ADAPTIVE PROJECTION ALGORITHM FOR MULTIRATE FILTER BANK OPTIMIZATION

to the nonquadratic nature of the cost function to be minimized, and accordingly non gradient algorithms may offer to the global minimum of the cost function, while at the same time avoiding potential local minima due to itsAN ADAPTIVE PROJECTION ALGORITHM FOR MULTIRATE FILTER BANK OPTIMIZATION Dong-Yan Huang and Phillip

Regalia, Phillip A.

362

Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem

have looked at the application of genetic algorithms to optimization of nonlinear functions; our algorithm works. The method operates with a set of potential solutions. This is referred to as a population individuals. Based on this fitness function a number of individuals are selected as potential parents

Ferris, Michael C.

363

Searching for Pareto-optimal Randomised Algorithms

, providing an avenue of optimisation to satisfy non-functional requirements. We use Multi that the satisfaction of non-functional requirements is an impor- tant consideration in algorithm design that the choice of probability distribution influences the non-functional prop- erties of such algorithms

White, David R.

364

Neural Networks Training with Optimal Bounded Ellipsoid Algorithm

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some\\u000a better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages\\u000a over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights\\u000a of recurrent neural

José De Jesús Rubio; Wen Yu

2007-01-01

365

Monotonically convergent algorithm for quantum optimal control with dissipation

NASA Astrophysics Data System (ADS)

This paper extends a monotonically convergent algorithm for quantum optimal control to treat systems with dissipation. The algorithm working with the density matrix is proved to exhibit quadratic and monotonic convergence. Several numerical tests are implemented in three-level model systems. The algorithm is exploited to control various targets, including the expectation value of a Hermitian operator, the modulus square of the expectation value of a non-Hermitian operator, and off-diagonal elements of the density matrix.

Ohtsuki, Yukiyoshi; Zhu, Wusheng; Rabitz, Herschel

1999-05-01

366

Design of underwater robot lines based on a hybrid automatic optimization strategy

NASA Astrophysics Data System (ADS)

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Lyu, Wenjing; Luo, Weilin

2014-09-01

367

Optimization of Hybrid Electric Cars by Neuro-Fuzzy Networks

In this paper, the problem of the optimization of energetic\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009flows in hybrid electric vehicles is faced. We consider a hybrid electric \\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009vehicle\\u0009equipped with batteries, a thermal engine (or fuel cells), ultracapacitors\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009and an electric engine. The energetic flows are optimized by using a\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009control strategy based on the prediction of short-term and medium-term\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009vehicle states (energy consumption, vehicle

Fabio Massimo Frattale Mascioli; Antonello Rizzi; Massimo Panella; Claudia Bettiol

2007-01-01

368

A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain

For estimating the states or outputs of a Markov process, the symbol-by-symbol MAP algorithm is optimal. However, this algorithm, even in its recursive form, poses technical difficulties because of numerical representation problems, the necessity of nonlinear functions and a high number of additions and multiplications. MAP like algorithms operating in the logarithmic domain presented in the past solve the numerical

P. Robertson; E. Villebrun; P. Hoeher

1995-01-01

369

NASA Astrophysics Data System (ADS)

The Dynamic economic dispatch (DED) problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFO algorithms with varying chemo tactic step size for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with those methods reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.

Praveena, P.; Vaisakh, K.; Rama Mohana Rao, S.

370

Enhanced Dynamic Programming Algorithms for Series Line Optimization

1 Enhanced Dynamic Programming Algorithms for Series Line Optimization Michael H. Veatch* May 2005 Abstract Dynamic programming value iteration is made more ef cient on a ve-machine unreliable series line of optimal policies are identi ed. Index Terms Make-to-stock, production line, dynamic programming, control

Veatch, Michael H.

371

Optimal distributed algorithm for minimum spanning trees revisited

In an earlier paper, Awerbuch presented an innovative distributedalgorithm for solving minimum spanning tree (MST)problems that achieved optimal time and message complexitythrough the introduction of several advanced features.In this paper, we show that there are some cases where hisalgorithm can create cycles or fail to achieve optimal timecomplexity. We then show how to modify the algorithm toavoid these problems, and

Michalis Faloutsos; Mart Molle

1995-01-01

372

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm

A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and\\/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility

Christoph Burger

2007-01-01

373

Model Specification Searches Using Ant Colony Optimization Algorithms

ERIC Educational Resources Information Center

Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.

Marcoulides, George A.; Drezner, Zvi

2003-01-01

374

A hybrid multiview stereo algorithm for modeling urban scenes.

We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial meshbased surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms. PMID:22487981

Lafarge, Florent; Keriven, Renaud; Brédif, Mathieu; Vu, Hoang-Hiep

2013-01-01

375

NASA Astrophysics Data System (ADS)

This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.

Nourelfath, M.; Nahas, N.; Montreuil, B.

2007-12-01

376

A Hybrid Quantum Search Engine: A Fast Quantum Algorithm for Multiple Matches

In this paper we will present a quantum algorithm which works very efficiently in case of multiple matches within the search space and in the case of few matches, the algorithm performs classically. This allows us to propose a hybrid quantum search engine that integrates Grover's algorithm and the proposed algorithm here to have general performance better that any pure classical or quantum search algorithm.

Ahmed Younes; Jon Rowe; Julian Miller

2003-11-25

377

Optimal Stochastic Approximation Algorithms for Strongly Convex ...

Jul 1, 2010 ... imate solutions 10 ? 40 times faster than an SAA based algorithm while keeping similar solution quality. ...... e,1 are in the same order of magnitude, i.e., O(1/. ?. N ). ...... Inference, and Prediction, Second Edition. Springer ...

2012-06-18

378

Optimization of the Solovay-Kitaev algorithm

NASA Astrophysics Data System (ADS)

The Solovay-Kitaev algorithm is the standard method used for approximating arbitrary single-qubit gates for fault-tolerant quantum computation. In this paper we introduce a technique called search space expansion, which modifies the initial stage of the Solovay-Kitaev algorithm, increasing the length of the possible approximating sequences but without requiring an exhaustive search over all possible sequences. This technique is combined with an efficient space search method called geometric nearest-neighbor access trees, modified for the unitary matrix lookup problem, in order to reduce significantly the algorithm run time. We show that, with low time cost, our techniques output gate sequences that are almost an order of magnitude smaller for the same level of accuracy. This therefore reduces the error correction requirements for quantum algorithms on encoded fault-tolerant hardware.

Pham, Tien Trung; Van Meter, Rodney; Horsman, Clare

2013-05-01

379

A Discrete Lagrangian Algorithm for Optimal Routing Problems

The ideas of discrete Lagrangian methods for conservative systems are exploited for the construction of algorithms applicable in optimal ship routing problems. The algorithm presented here is based on the discretisation of Hamilton's principle of stationary action Lagrangian and specifically on the direct discretization of the Lagrange-Hamilton principle for a conservative system. Since, in contrast to the differential equations, the discrete Euler-Lagrange equations serve as constrains for the optimization of a given cost functional, in the present work we utilize this feature in order to minimize the cost function for optimal ship routing.

Kosmas, O. T.; Vlachos, D. S.; Simos, T. E. [University of Peloponnese, 22100 Tripoli (Greece)

2008-11-06

380

Air data system optimization using a genetic algorithm

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

381

Integrated genetic algorithm for optimization of space structures

NASA Astrophysics Data System (ADS)

Gradient-based mathematical-optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty-function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global-searches. The technique employs the Darwinian survival-of-the-fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to the optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.

Adeli, Hojjat; Cheng, Nai-Tsang

1993-10-01

382

Scaled conjugate gradient algorithms for unconstrained optimization

In this work we present and analyze a new scaled conjugate gradient algorithm and its implementation, based on an interpretation\\u000a of the secant equation and on the inexact Wolfe line search conditions. The best spectral conjugate gradient algorithm SCG\\u000a by Birgin and Martínez (2001), which is mainly a scaled variant of Perry’s (1977), is modified in such a manner to

Neculai Andrei

2007-01-01

383

A novel optimization sizing model for hybrid solar-wind power generation system

This paper develops the Hybrid Solar-Wind System Optimization Sizing (HSWSO) model, to optimize the capacity sizes of different components of hybrid solar-wind power generation systems employing a battery bank. The HSWSO model consists of three parts: the model of the hybrid system, the model of Loss of Power Supply Probability (LPSP) and the model of the Levelised Cost of Energy

Hongxing Yang; Lin Lu; Wei Zhou

2007-01-01

384

Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle

Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle Namwook Kim. INTRODUCTION he optimal control of HEVs (Hybrid Electric Vehicles) is an important topic not only because, Sukwon Cha, Huei Peng Abstract - A number of strategies for the power management of HEVs (Hybrid Electric

Peng, Huei

385

Optimization and Comparison of Heuristic Control Strategies for Parallel Hybrid-Electric Vehicles

Optimization and Comparison of Heuristic Control Strategies for Parallel Hybrid-Electric Vehicles independent. Thus, these control strategies are predestinated for the use in a real vehicle. Keywords: Hybrid-electric vehicle (HEV), control strategies, optimization. 1. Introduction Due to the structure of hybrid-electric

Paderborn, UniversitÃ¤t

386

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

NASA Astrophysics Data System (ADS)

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.

Zhang, Xin-Li; Zhang, Ke-Cun

2009-09-01

387

Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790

Le Dinh, Luong; Vo Ngoc, Dieu

2013-01-01

388

Artificial bee colony algorithm for solving optimal power flow problem.

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790

Le Dinh, Luong; Vo Ngoc, Dieu; Vasant, Pandian

2013-01-01

389

Global Optimization of Plug-In Hybrid Vehicle Design and Allocation to

- tion of conventional (CV), hybrid electric (HEV), and plug-in hybrid electric (PHEV) vehicles to obtain assessment 1 Introduction Plug-in hybrid electric vehicles (PHEVs) offer a potentially promising technologyGlobal Optimization of Plug-In Hybrid Vehicle Design and Allocation to Minimize Life Cycle

Michalek, Jeremy J.

390

A near optimal algorithm for lifetime optimization in wireless sensor networks

A near optimal algorithm for lifetime optimization in wireless sensor networks Karine Deschinkel1.deschinkel, mourad.hakem}@univ-fcomte.fr Keywords: target coverage, wireless sensor networks, centralized method in wireless sensor networks (WSN) is lifetime optimization. Indeed, in WSN each sensor node is battery powered

Paris-Sud XI, Université de

391

In the present paper, the simulation and optimization of asynchronous AC motor through its controlling and modeling by convert d-q with Simulink of Mat lab Software has been studied. By utilization of classic PI controller and also with phase controller has been optimized which membership function center of it has been optimized by new intelligent algorithms such as Emperor and

Masoud Nabipour Afrozi; Masoud Hassanpour Aghdam; Ahmad Naebi; Saeed Hassanpour Aghdam

2011-01-01

392

An Ant Colony Optimization Algorithm for the Optimization of the Keyboard Arrangement

An Ant Colony Optimization Algorithm for the Optimization of the Keyboard Arrangement Problem Jan account of ergonomic criteria is proposed. Based on the generic framework of Ant Colony Optimiza- tion computations, ant colony optimization, key- board arrangement 1 Introduction A computer user or typist

Paris-Sud XI, Université de

393

A Parallel Tempering algorithm for probabilistic sampling and multimodal optimization

NASA Astrophysics Data System (ADS)

Non-linear inverse problems in the geosciences often involve probabilistic sampling of multimodal density functions or global optimization and sometimes both. Efficient algorithmic tools for carrying out sampling or optimization in challenging cases are of major interest. Here results are presented of some numerical experiments with a technique, known as Parallel Tempering, which originated in the field of computational statistics but is finding increasing numbers of applications in fields ranging from Chemical Physics to Astronomy. To date, experience in use of Parallel Tempering within earth sciences problems is very limited. In this paper, we describe Parallel Tempering and compare it to related methods of Simulated Annealing and Simulated Tempering for optimization and sampling, respectively. A key feature of Parallel Tempering is that it satisfies the detailed balance condition required for convergence of Markov chain Monte Carlo (McMC) algorithms while improving the efficiency of probabilistic sampling. Numerical results are presented on use of Parallel Tempering for trans-dimensional inversion of synthetic seismic receiver functions and also the simultaneous fitting of multiple receiver functions using global optimization. These suggest that its use can significantly accelerate sampling algorithms and improve exploration of parameter space in optimization. Parallel Tempering is a meta-algorithm which may be used together with many existing McMC sampling and direct search optimization techniques. It's generality and demonstrated performance suggests that there is significant potential for applications to both sampling and optimization problems in the geosciences.

Sambridge, Malcolm

2014-01-01

394

An Improved PSO Algorithm and its Application to Structural Fatigue Life Optimization

Particle swarm optimization is a new type of intelligent optimization algorithm. In order to improve the efficiency of structural fatigue optimization, a novel optimization strategy based on the improved particle swarm optimization algorithm is proposed. According to this strategy, one optimization software is developed. As an application of this strategy, the fatigue optimization of a landing gear is presented. The

Liu Bing; Xue Caijun; Tan Wei

2010-01-01

395

A hybrid evolutionary learning algorithm for TSK-type fuzzy model design

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals

Cheng-Jian Lin; Yong-Ji Xu

2006-01-01

396

Recursive hybrid algorithm for non-linear system identification using radial basis function networks

Recursive identification of non-linear systems is investigated using radial basis function networks. A novel approach is adopted which employs a hybrid clustering and least squares algorithm. The recursive clustering algorithm adjusts the centres of the radial basis function network while the recursive least squares algorithm estimates the connection weights of the network. Because these two recursive learning rules are both

S. CHEN; S. A. BILLINGS; P. M. GRANT

1992-01-01

397

A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme

NASA Astrophysics Data System (ADS)

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a nonconventional supersonic tailless air vehicle wing shape optimization problem.

Ghoman, Satyajit S.

398

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms Sergio of jazz standards, and we collected commercial audio recordings extracted from jazz guitar CDs. Based on the MIDI record- ings as ground truth, two different instrument settings are compared (Jazz trio

399

Design optimization of gas generator hybrid propulsion boosters

NASA Technical Reports Server (NTRS)

A methodology used in support of a contract study for NASA/MSFC to optimize the design of gas generator hybrid propulsion booster for uprating the National Space Transportation System (NSTS) is presented. The objective was to compare alternative configurations for this booster approach, optimizing each candidate concept on different bases, in order to develop data for a trade table on which a final decision was based. The methodology is capable of processing a large number of independent and dependent variables, adjusting the overall subsystems characteristics to arrive at a best compromise integrated design to meet various specified optimization criteria subject to selected constraints. For each system considered, a detailed weight statement was generated along with preliminary cost and reliability estimates.

Weldon, Vincent; Phillips, Dwight U.; Fink, Lawrence E.

1990-01-01

400

Design Optimization of Gas Generator Hybrid Propulsion Boosters

NASA Technical Reports Server (NTRS)

A methodology used in support of a study for NASA/MSFC to optimize the design of gas generator hybrid propulsion booster for uprating the National Space Transportation System (NSTS) is presented. The objective was to compare alternative configurations for this booster approach, optimizing each candidate concept on different bases, in order to develop data for a trade table on which a final decision was based. The methodology is capable of processing a large number of independent and dependent variables, adjusting the overall subsystems characteristics to arrive at a best compromise integrated design to meet various specific optimization criteria subject to selected constraints. For each system considered, a detailed weight statement was generated along with preliminary cost and reliability estimates.

Weldon, Vincent; Phillips, Dwight; Fink, Larry

1990-01-01

401

Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios

NASA Astrophysics Data System (ADS)

Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.

Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher

2005-08-01

402

A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of “value performance,” the “ordinal performance” is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and “good enough” set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. PMID:25013845

Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

403

sp3-hybridized framework structure of group-14 elements discovered by genetic algorithm

Group-14 elements, including C, Si, Ge, and Sn, can form various stable and metastable structures. Finding new metastable structures of group-14 elements with desirable physical properties for new technological applications has attracted a lot of interest. Using a genetic algorithm, we discovered a new low-energy metastable distorted sp3-hybridized framework structure of the group-14 elements. It has P42/mnm symmetry with 12 atoms per unit cell. The void volume of this structure is as large as 139.7Å3 for Si P42/mnm, and it can be used for gas or metal-atom encapsulation. Band-structure calculations show that P42/mnm structures of Si and Ge are semiconducting with energy band gaps close to the optimal values for optoelectronic or photovoltaic applications. With metal-atom encapsulation, the P42/mnm structure would also be a candidate for rattling-mediated superconducting or used as thermoelectric materials.

Nguyen, Manh Cuong [Ames Laboratory; Zhao, Xin [Ames Laboratory; Wang, Cai-Zhuang [Ames Laboratory; Ho, Kai-Ming [Ames Laboratory

2014-05-01

404

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Zhu, Zhouquan

2013-01-01

405

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

406

This paper presents the genetic representation in the developed hybrid evolutionary algorithm, applied for real-world case of flexible job shop scheduling problem. The hybrid evolutionary algorithm, which combines priority-dispatching rules (PDRs) with genetic algorithms (GA), is discussed. PDRs offer the advantage of simplicity and low computational cost. GA incorporated into proposed algorithm addresses the myopic nature of PDRs and the

Ivan T. Tanev; Takashi Uozumi; Yoshiharu Morotome

407

A training algorithm for optimal margin classifiers

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of

Bernhard E. Boser; Isabelle M. Guyon; Vladimir N. Vapnik

1992-01-01

408

Parallel Algorithms for Big Data Optimization

at least one (block) component which is within a factor ? ? (0, 1] “far ..... affect in any way the theoretical convergence properties of the algorithms. On the other .... in comparison with parallel methods; therefore we excluded. ADMM and GS in .... because we already ascertained that they are not competitive. The tuning of the ...

2014-02-21

409

Optimal Speedup of Las Vegas Algorithms

Let A be a Las Vegas algorithm, i.e., A is a randomized algorithmthat always produces the correct answer when it stops but whose runningtime is a random variable. We consider the problem of minimizingthe expected time required to obtain an answer from A using strategieswhich simulate A as follows: run A for a fixed amount of timet 1 , then

Michael Luby; Alistair Sinclair; David Zuckerman

1993-01-01

410

Turbo codes optimization using genetic algorithms

Turbo codes have been an important revolution in the digital communications world. Since their discovery, the coding community has been trying to understand, explain and improve turbo codes. The floor phenomenon is the parallel concatenated convolutional turbo codes main problem. In this paper, genetic algorithms are used to lower the free distance of such a code. Results in terms of

Nicolas Durand; Jean-Marc Alliot; B. Bartolome

1999-01-01

411

Binary wavefront optimization using a genetic algorithm

NASA Astrophysics Data System (ADS)

We demonstrate the use of a genetic algorithm with binary amplitude modulation of light through turbid media. We apply this method to binary amplitude modulation with a digital micromirror device. We achieve the theoretical maximum enhancement of 64 with 384 segments, and an enhancement of 320 with 6144 segments.

Zhang, Xiaolong; Kner, Peter

2014-12-01

412

Single-objective optimization of thermo-electric coolers using genetic algorithm

NASA Astrophysics Data System (ADS)

Thermo-electric Coolers (TECs) nowadays is applied in a wide range of thermal energy systems. This is due to its superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environment friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) to optimize geometry properties so that TECs will operate at near optimal conditions. In the future works, multi-objective optimization problems (MOP) using hybrid GA with another optimization technique will be considered to give a better results and compare with previous research such as Non-Dominated Sorting Genetic Algorithm (NSGA-II) to see the advantages and disadvantages.

Khanh, Doan V. K.; Vasant, P.; Elamvazuthi, Irraivan; Dieu, Vo N.

2014-10-01

413

Optimization of mass spectrometers using the adaptive particle swarm algorithm.

Optimization of mass spectrometers using the adaptive particle swarm algorithm (APSA) is described along with implementations for ion optical simulations and various time-of-flight (TOF) instruments. The need for in situ self optimization is addressed through discussion of the reflectron TOF mass spectrometer (RTOF) on the European Space Agency mission Rosetta. In addition, a tool for optimization of laboratory mass spectrometers is presented and tested on two different instruments. After the application of APSA optimization, a substantial increase in performance for mass spectrometers that have manually been tuned for several weeks or months is demonstrated. PMID:22124986

Bieler, A; Altwegg, K; Hofer, L; Jäckel, A; Riedo, A; Sémon, T; Wahlström, P; Wurz, P

2011-11-01

414

Modified ant-colony-optimization algorithm as an alternative to genetic algorithms

NASA Astrophysics Data System (ADS)

An alternative approach for the optimization strategy in quantum control experiments is proposed. Genetic algorithms are used frequently to improve the laser fields driving quantum processes. We present a flexible scheme based on ant-colony-optimization, which introduces a correlation of the mask function pixels and allows a decrease in the shaped pulse complexity and duration without loss of efficiency.

Gollub, C.; de Vivie-Riedle, R.

2009-02-01

415

Model predictive control of nonlinear hybrid system based on neural network optimization

This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark

Liyan Zhang; Shuhai Quan

2009-01-01

416

Design of a Lithium-ion Battery Pack for PHEV Using a Hybrid Optimization Method

Design of a Lithium-ion Battery Pack for PHEV Using a Hybrid Optimization Method Nansi Xue1 Abstract This paper outlines a method for optimizing the design of a lithium-ion battery pack for hy- brid, volume or material cost. Keywords: Lithium-ion, Optimization, Hybrid vehicle, Battery pack design

Papalambros, Panos

417

Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. PMID:25181467

Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

2015-02-01

418

A novel parallel hybrid electric vehicle (PHEV) configuration consisting of an extra one-way clutch and an automatic mechanical\\u000a transmission (AMT) is taken as the study subject of this paper. An energy management strategy (EMS) combining a logic threshold\\u000a approach and an instantaneous optimization algorithm is developed for the investigated PHEV. The objective of this EMS is\\u000a to achieve acceptable vehicle

Y.-J. Huang; C.-L. Yin; J.-W. Zhang

2009-01-01

419

NASA Astrophysics Data System (ADS)

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.

Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan

2014-12-01

420

Fixed structure compensator design using a constrained hybrid evolutionary optimization approach.

This paper presents an efficient technique for designing a fixed order compensator for compensating current mode control architecture of DC-DC converters. The compensator design is formulated as an optimization problem, which seeks to attain a set of frequency domain specifications. The highly nonlinear nature of the optimization problem demands the use of an initial parameterization independent global search technique. In this regard, the optimization problem is solved using a hybrid evolutionary optimization approach, because of its simple structure, faster execution time and greater probability in achieving the global solution. The proposed algorithm involves the combination of a population search based optimization approach i.e. Particle Swarm Optimization (PSO) and local search based method. The op-amp dynamics have been incorporated during the design process. Considering the limitations of fixed structure compensator in achieving loop bandwidth higher than a certain threshold, the proposed approach also determines the op-amp bandwidth, which would be able to achieve the same. The effectiveness of the proposed approach in meeting the desired frequency domain specifications is experimentally tested on a peak current mode control dc-dc buck converter. PMID:24768082

Ghosh, Subhojit; Samanta, Susovon

2014-07-01

421

Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

NASA Technical Reports Server (NTRS)

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)

2002-01-01

422

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

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.

Zeigler, B. P.

1974-01-01

423

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

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.

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

2008-01-01

424

Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces

NASA Technical Reports Server (NTRS)

Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn.

Martin, Rodney A.; Wheeler, Kevin R.; SunSpiral, Vytas; Allan, Mark B.

2006-01-01

425

Coordinate Search Algorithms in Multilevel Optimization

rithms in a multilevel optimization paradigm. We develop a .... discuss the issue of selecting the operators, and adopt as a default choice the linear ..... ? = ?t2 is a classical forcing function which ensures sufficient decrease; we set ? = 10?4 in ...

2012-10-16

426

An optimal extraction algorithm for imaging photometry

This paper is primarily an investigation of whether the `optimal extraction' techniques used in CCD spectroscopy can be applied to imaging photometry. It is found that using such techniques provides a gain of around 10 per cent in signal-to-noise ratio over normal aperture photometry. Formally, it is shown to be equivalent to profile fitting, but offers advantages of robust error

Tim Naylor

1998-01-01

427

Initializing Partition-Optimization Algorithms Ranjan Maitra

is a challenging problem needed in a wide array of applications. Partition-optimization ap- proaches, such as k applicability of the problem. Most approaches involve a certain degree of empiricism but broadly fall- like structure for demarcating groups, with the property that all observations in a group at some

Maitra, Ranjan

428

A software-optimized encryption algorithm

We describe a fast, software-oriented, encryption algorithm. Computational cost on a 32-bit processor is about 5 elementary machine instructions per byte of text. The cipher is a pseudorandom function; under control of a key (first pre-processed into an internal table) it stretches a short index into a much longer pseudorandom string. This string can be used as a one-time pad.

Phillip Rogaway; Don Coppersmith

429

Parallel Particle Swarm Optimization Algorithm Based on Graphic Processing Units

\\u000a A novel parallel approach to implement particle swarm optimization(PSO) algorithm on graphic processing units(GPU) in a personal\\u000a computer is proposed in this chapter. By using the general-purpose computing ability of GPU and under the software platform\\u000a of compute unified device architecture(CUDA) which is developed by NVIDIA, the PSO algorithm can be executed in parallel on\\u000a the GPU. The process of

Ying Tan; You Zhou

430

An optimal on-line algorithm for metrical task system

In practice, almost all dynamic systems require decisions to be made on-line, without full knowledge of their future impact on the system. A general model for the processing of sequences of tasks is introduced, and a general on-line decision algorithm is developed. It is shown that, for an important class of special cases, this algorithm is optimal among all on-line

Allan Borodin; Nathan Linial; Michael E. Saks

1992-01-01

431

Modeling and optimization of a hybrid solar combined cycle (HYCS)

NASA Astrophysics Data System (ADS)

The main objective of this thesis is to investigate the feasibility of integrating concentrated solar power (CSP) technology with the conventional combined cycle technology for electric generation in Saudi Arabia. The generated electricity can be used locally to meet the annual increasing demand. Specifically, it can be utilized to meet the demand during the hours 10 am-3 pm and prevent blackout hours, of some industrial sectors. The proposed CSP design gives flexibility in the operation system. Since, it works as a conventional combined cycle during night time and it switches to work as a hybrid solar combined cycle during day time. The first objective of the thesis is to develop a thermo-economical mathematical model that can simulate the performance of a hybrid solar-fossil fuel combined cycle. The second objective is to develop a computer simulation code that can solve the thermo-economical mathematical model using available software such as E.E.S. The developed simulation code is used to analyze the thermo-economic performance of different configurations of integrating the CSP with the conventional fossil fuel combined cycle to achieve the optimal integration configuration. This optimal integration configuration has been investigated further to achieve the optimal design of the solar field that gives the optimal solar share. Thermo-economical performance metrics which are available in the literature have been used in the present work to assess the thermo-economic performance of the investigated configurations. The economical and environmental impact of integration CSP with the conventional fossil fuel combined cycle are estimated and discussed. Finally, the optimal integration configuration is found to be solarization steam side in conventional combined cycle with solar multiple 0.38 which needs 29 hectare and LEC of HYCS is 63.17 $/MWh under Dhahran weather conditions.

Eter, Ahmad Adel

2011-12-01

432

Hybrid Biogeography-Based Optimization for Integer Programming

Biogeography-based optimization (BBO) is a relatively new bioinspired heuristic for global optimization based on the mathematical models of biogeography. By investigating the applicability and performance of BBO for integer programming, we find that the original BBO algorithm does not perform well on a set of benchmark integer programming problems. Thus we modify the mutation operator and/or the neighborhood structure of the algorithm, resulting in three new BBO-based methods, named BlendBBO, BBO_DE, and LBBO_LDE, respectively. Computational experiments show that these methods are competitive approaches to solve integer programming problems, and the LBBO_LDE shows the best performance on the benchmark problems. PMID:25003142

Wang, Zhi-Cheng

2014-01-01

433

A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping

NASA Astrophysics Data System (ADS)

In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.

Jiang, Wenbo; Wang, Jun; Dong, Xiucheng

2013-02-01

434

Shape Optimization of Rubber Bushing Using Differential Evolution Algorithm

The objective of this study is to design rubber bushing at desired level of stiffness characteristics in order to achieve the ride quality of the vehicle. A differential evolution algorithm based approach is developed to optimize the rubber bushing through integrating a finite element code running in batch mode to compute the objective function values for each generation. Two case studies were given to illustrate the application of proposed approach. Optimum shape parameters of 2D bushing model were determined by shape optimization using differential evolution algorithm. PMID:25276848

2014-01-01

435

New near-optimal feedback guidance algorithms for space missions

NASA Astrophysics Data System (ADS)

This dissertation describes several different spacecraft guidance algorithms, with applications including asteroid intercept and rendezvous, planetary landing, and orbital transfer. A comprehensive review of spacecraft guidance algorithms for asteroid intercept and rendezvous. Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) guidance is introduced and applied to asteroid intercept and rendezvous, and to a wealth of different example problems, including missile intercept, planetary landing, and orbital transfer. It is seen that the ZEM/ZEV guidance law can be used in many different scenarios, and that it provides near-optimal performance where an analytical optimal guidance law does not exist, such as in a non-linear gravity field.

Hawkins, Matthew Jay

436

Series hybrid vehicles and optimized hydrogen engine design

Lawrence Livermore, Sandia Livermore and Los Alamos National Laboratories have a joint project to develop an optimized hydrogen fueled engine for series hybrid automobiles. The major divisions of responsibility are: system analysis, engine design and kinetics modeling by LLNL; performance and emission testing, and friction reduction by SNL; computational fluid mechanics and combustion modeling by LANL. This project is a component of the Department of Energy, Office of Utility Technology, National Hydrogen Program. We report here on the progress on system analysis and preliminary engine testing. We have done system studies of series hybrid automobiles that approach the PNGV design goal of 34 km/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. The impact of various on-board storage options on fuel economy are evaluated. Experiments with an available engine at the Sandia Combustion Research Facility demonstrated NO{sub x} emissions of 10 to 20 ppm at an equivalence ratio of 0.4, rising to about 500 ppm at 0.5 equivalence ratio using neat hydrogen. Hybrid vehicle simulation studies indicate that exhaust NO{sub x} concentrations must be less than 180 ppm to meet the 0.2 g/mile California Air Resources Board ULEV or Federal Tier II emissions regulations. We have designed and fabricated a first generation optimized hydrogen engine head for use on an existing single cylinder Onan engine. This head currently features 14.8:1 compression ratio, dual ignition, water cooling, two valves and open quiescent combustion chamber to minimize heat transfer losses.

Smith, J.R.; Aceves, S. [Lawrence Livermore National Lab., CA (United States); Van Blarigan, P. [Sandia National Labs., Livermore, CA (United States)

1995-05-10

437

Hybrid robust predictive optimization method of power system dispatch

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

Chandra, Ramu Sharat (Niskayuna, NY); Liu, Yan (Ballston Lake, NY); Bose, Sumit (Niskayuna, NY); de Bedout, Juan Manuel (West Glenville, NY)

2011-08-02

438

On the optimality of the neighbor-joining algorithm

The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is "optimal" when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are determined by polyhedral subdivisions of the spaces of dissimilarity maps R+(n2) to study the optimality of the neighbor-joining algorithm. In particular, we investigate and compare the polyhedral subdivisions for n ? 8. This requires the measurement of volumes of spherical polytopes in high dimension, which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. Our results include a demonstration that highly unrelated trees can be co-optimal in BME reconstruction, and that NJ regions are not convex. We obtain the l2 radius for neighbor-joining for n = 5 and we conjecture that the ability of the neighbor-joining algorithm to recover the BME tree depends on the diameter of the BME tree. PMID:18447942

Eickmeyer, Kord; Huggins, Peter; Pachter, Lior; Yoshida, Ruriko

2008-01-01

439

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

NASA Technical Reports Server (NTRS)

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

440

Optimization Algorithm for the Generation of ONCV Pseudopotentials

We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z=83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials provide a good agreement with the all-electron results obtained using the FLEUR code with a plane-wave energy cutoff of approximately 60 Ry.

Schlipf, Martin

2015-01-01

441

Chaos Time Series Prediction Based on Membrane Optimization Algorithms

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (?, m) and least squares support vector machine (LS-SVM) (?, ?) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Li, Meng; Yi, Liangzhong; Pei, Zheng; Gao, Zhisheng

2015-01-01

442

Hybrid photoneutron source optimization for electron accelerator-based BNCT

NASA Astrophysics Data System (ADS)

Boron Neutron Capture Therapy (BNCT) is being studied as a possible radiotherapic treatment for some cancer types. Neutron energy for penetrating into tissue should be in the epithermal range. Different methods are used for neutron production. Electron accelerators are an alternative way for producing neutrons in electron-photon-neutron processes. Optimization of electron/photon and photoneutron targets calculations with respect to electron energy, dimension (radius and thickness) and neutron yield were done by MCNPX Monte Carlo code. According to the results, a hybrid photoneutron source including BeD 2 and Tungsten has been introduced.

Rahmani, F.; Shahriari, M.

2010-06-01

443

In this paper an improved ant colony algorithm is presented and an algorithm in combination with particle swarm optimization algorithm and the improved ant colony algorithm for multi-objective flexible job shop scheduling problem are employed. The algorithm proposed in this paper includes two parts. The first part makes use of the fast convergence of PSO to search the particles optimum

Li Li; Wang Keqi; Zhou Chunnan

2010-01-01

444

Hybrid Application On Job-Shop Scheduling by Genetic Algorithm and MAS Spring-Net

To solve job-shop production scheduling problem, we presented a new hybrid optimum arithmetic of using genetic algorithm and MAS(multi-agent system) composite-spring-net. Introduced the structure and model of genetic algorithm, illuminated the basic principle of spring-net arithmetic. By the experiment proving, the hybrid application of GA and sping-net arithmetic can make favorable effect on solving the problems of job-shop schedule.

Dong Yumin; Xiao Shufen

2007-01-01

445

A particle swarm optimization algorithm for balancing assembly lines

Purpose – The purpose of this paper is to apply particle swarm optimization (PSO) a known combinatorial optimization algorithm to multi-objective (MO) balancing of large assembly lines. Design\\/methodology\\/approach – A novel approach based on PSO is developed to tackle the simple assembly line balancing problem (SALBP), a well-known NP-hard production and operations management problem. Line balancing is considered for two-criteria

Dimitris I. Petropoulos; Andreas C. Nearchou

2011-01-01

446

Efficient and extensible algorithms for multi query optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multiquery optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were

Prasan Roy; S. Seshadri; S. Sudarshan; Siddhesh Bhobe

2000-01-01

447

Efficient and Extensible Algorithms for Multi Query Optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multi- query optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms

Prasan Roy; S. Seshadri; S. Sudarshan; Siddhesh Bhobe

2000-01-01

448

Faster optimal parallel prefix circuits: New algorithmic construction

Parallel prefix circuits are parallel prefix algorithms on the combinational circuit model. A prefix circuit with n inputs is depth-size optimal if its depth plus size equals 2n-2. Smaller depth implies faster computation, while smaller size implies less power consumption, less VLSI area, and less cost. To be of practical use, the depth and fan-out of a depth-size optimal prefix

Yen-chun Lin; Chin-yu Su

2005-01-01

449

Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint

In many engineering applications we deal with constrained optimization problems with respect to complex-valued matrices. This paper proposes a Riemannian geometry approach for optimization of a real-valued cost function T of complex-valued matrix argument W, under the constraint that W is an n times n unitary matrix. We derive steepest descent (SD) algorithms on the Lie group of unitary matrices

Traian E. Abrudan; Jan Eriksson; Visa Koivunen

2008-01-01

450

Seeker Optimization Algorithm for Digital IIR Filter Design

Since the error surface of digital infinite-impulse-response (IIR) filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a seeker-optimization-algorithm (SOA)-based evolutionary method is proposed for digital IIR filter design. SOA is based on the concept of simulating the act of human searching in which the search direction is based

Chaohua Dai; Weirong Chen; Yunfang Zhu

2010-01-01

451

NASA Astrophysics Data System (ADS)

This paper introduces an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on analytic analysis between fuel-rate and battery current at different driveline power and vehicle speed, quadratic equations are applied to simulate the relationship between battery current and vehicle fuel-rate. The power threshold at which engine is turned on is optimized by genetic algorithm (GA) based on vehicle fuel-rate, battery state of charge (SOC) and driveline power demand. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. Moreover, the controller is still applicable when the battery is unhealthy. Numerical simulations validated the feasibility of the proposed controller.

Chen, Zheng; Mi, Chris Chunting; Xiong, Rui; Xu, Jun; You, Chenwen

2014-02-01

452

NASA Astrophysics Data System (ADS)

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

Hassan, Md. Rakib; Islam, Md. Monirul; Murase, Kazuyuki

453

Rational function optimization using genetic algorithms

NASA Astrophysics Data System (ADS)

In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates.

Valadan Zoej, M. J.; Mokhtarzade, M.; Mansourian, A.; Ebadi, H.; Sadeghian, S.

2007-12-01

454

NASA Astrophysics Data System (ADS)

Energy management(EM) is a core technique of hybrid electric bus(HEB) in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization(PBIO) energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus(SPHEB) dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

Sun, Dongye; Lin, Xinyou; Qin, Datong; Deng, Tao

2012-11-01

455

A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)

NASA Astrophysics Data System (ADS)

Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.

Cantó, J.; Curiel, S.; Martínez-Gómez, E.

2009-07-01

456

A multiobjective memetic algorithm based on particle swarm optimization.

In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution. PMID:17278557

Liu, Dasheng; Tan, K C; Goh, C K; Ho, W K

2007-02-01

457

Optimal brushless DC motor design using genetic algorithms

NASA Astrophysics Data System (ADS)

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.

Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.

2010-11-01

458

Optimal Placement and Sizing of Distributed Generator Units using Genetic Optimization Algorithms

In this article the authors describe how genetic optimization algorithms can be used to find the optimal size and location of distributed generation units in a residential distri- bution grid. Power losses are minimized while the voltage profile is kept at an acceptable level. The method is applied on a system based on an existing grid topology with pro- duction

Edwin Haesen; Marcelo Espinoza; Bert Pluymers; Ivan Goethals; Vu Van Thong; Johan Driesen; Ronnie Belmans; Bart De Moor

459

Optimal dispatch strategy in remote hybrid power systems

For small villages in developing countries, local stand-alone power systems are often more cost-effective than utility grid extension. Various combinations of wind turbine generators, photovoltaic arrays, diesel gensets, and batteries - remote hybrid power systems - may be preferred to diesel-only systems. Dispatch strategy is the aspect of control strategy that pertains to energy flows among components. In systems with both batteries and diesel genset(s), dispatch affects the life-cycle cost through both the fuel usage and the battery life. In this study, dispatch strategies are compared using (1) an analysis of cost trade-offs, (2) a simple, quasi-steady-state time-series model, and finally (3) HYBRID2, a more sophisticated stochastic time-series model. An idealized predictive dispatch strategy, based on assumed perfect knowledge of future load and wind conditions, is developed and used as a benchmark in evaluating simple, non-predictive strategies. The results illustrate the nature of the optimal strategy and indicate that one of two simple diesel dispatch strategies - either load-following or full power for a minimum run time - can, in conjunction with the frugal use of stored energy (the Frugal Discharge Strategy), be virtually as cost-effective as the Ideal Predictive Strategy. The optimal choice of these two simple charging strategies is correlated to three dimensionless parameters, yielding a generalized dispatch design chart for an important class of systems. 30 refs., 17 figs., 3 tabs.

Barley, C.D.; Winn, C.B. [Colorado State Univ., Fort Collins, CO (United States)

1996-10-01

460

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

. Sequential quadratic programming (SQP) methods have proved highly effective forsolving constrained optimization problems with smooth nonlinear functions in the objective andconstraints. Here we consider problems with general inequality constraints (linear and nonlinear).We assume that first derivatives are available, and that the constraint gradients are sparse.We discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function andmakes explicit

Philip E. Gill; Walter Murray; Michael A. Saunders

1997-01-01

461

A CONDITIONAL GAUSSIAN MARTINGALE ALGORITHM FOR GLOBAL OPTIMIZATION

is to change, at each new repetition, the location and dispersion parameters of the probability GaussianA CONDITIONAL GAUSSIAN MARTINGALE ALGORITHM FOR GLOBAL OPTIMIZATION MANUEL L. ESQUÂ´IVEL Abstract be thought to belong to the random search class but although we use Gaussian distributions at each repetition

Lisbon, University of

462

Attitude determination using vector observations: A fast optimal matrix algorithm

NASA Technical Reports Server (NTRS)

The attitude matrix minimizing Wahba's loss function is computed directly by a method that is competitive with the fastest known algorithm for finding this optimal estimate. The method also provides an estimate of the attitude error covariance matrix. Analysis of the special case of two vector observations identifies those cases for which the TRIAD or algebraic method minimizes Wahba's loss function.

Markley, F. Landis

1993-01-01

463

Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization \\Lambda

trillion standard cubic feet of natural gas per year, representing roughly a third of worldwide consumption consider minimization of the cost of fuel and/or electric power for the compressor stations in a gasAlgorithms for Noisy Problems in Gas Transmission Pipeline Optimization \\Lambda R. G. Carter y J. M

464

Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization

trillion standard cubic feet of natural gas per year, representing roughly a third of worldwide consumption minimization of the cost of fuel and/or electric power for the compressor stations in a gas pipeline networkAlgorithms for Noisy Problems in Gas Transmission Pipeline Optimization R. G. Cartery J. M

465

Optimal and Practical Algorithms for Sorting on the PDM

The Parallel Disks Model (PDM) has been proposed to alleviate the I\\/O bottle- neck that arises in the processing of massive data sets. Sorting has been extensively studied on the PDM model due to the fundamental nature of the problem - several asymptotically optimal algorithms are known for sorting. Although randomization has been frequently ex- ploited, most of the prior

Sanguthevar Rajasekaran; Sandeep Sen

2008-01-01

466

Optimal Scheduling of Booster Chlorination with Immune Algorithm

This paper describes the methodology and application of an immune algorithm (IA) scheme tailor-made for the EPANET for simultaneously optimizing the injection rates and scheduling of chlorine booster stations under the unsteady state of a water distribution network system (WDNS). The objective of this study is to initiate a total chlorination dose to satisfy the minimum and maximum required chlorine

Chien-Wei Chu; Min-Der Lin; Kang-Ting Tsai

2008-01-01

467

8. Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics

: the reconstruction of evolution- ary histories (phylogenies) from molecular data such as DNA sequences. Our8. Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics Bernard M. E an extensive study of quartet-based reconstruction algo- rithms within a parameter-rich simulation space, using

Moret, Bernard

468

GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS

This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA

David L. Carroll

1996-01-01

469

A new particle swarm optimization algorithm for dynamic image clustering

In this paper, we present ACPSO a new dynamic image clustering algorithm based on particle swarm optimization. ACPSO can partition image into compact and well separated clusters without any knowledge on the real number of clusters. It uses a swarm of particles with variable number of length, which evolve dynamically using mutation operators. Experimental results on real images demonstrate that

Salima Ouadfel; Mohamed Batouche; Abdelmalik Taleb-Ahmed

2010-01-01

470

Intelligent evolutionary algorithms for large parameter optimization problems

This work proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents

Shinn-ying Ho; Li-sun Shu; Jian-hung Chen

2004-01-01

471

Optimization flow control—I: basic algorithm and convergence

We propose an optimization approach to o w control where the objective is to maximize the aggregate source utility over their transmission rates. We view net- work links and sources as processors of a distributed com- putation system to solve the dual problem using gradient projection algorithm. In this system sources select trans- mission rates that maximize their own benets,

Steven H. Low; David E. Lapsley

1999-01-01

472

Optimal Algorithms for Well-Conditioned Nonlinear Systems of Equations

of systems of ordinary differential equations. More precisely, the numerical inte- gration of the classicalOptimal Algorithms for Well-Conditioned Nonlinear Systems of Equations Monica Bianchini, Stefano Fanelli, and Marco Gori, Fellow, IEEE AbstractÐWe propose solving nonlinear systems of equations

Fanelli, Stephen

473

Comparison of probabilistic and deterministic optimizations using genetic algorithms

This paper describes an application of genetic algorithms to deterministic and probabilistic (reliability-based) optimization of damping augmentation for a truss structure. The probabilistic formulation minimizes the probability of exceeding upper limits on the magnitude of the dynamic response of the structure due to uncertainties in the properties of the damping devices. The corresponding deterministic formulation maximizes a safety margin with

E. Ponslet; G. Maglaras; R. T. Haftka; E. Nikolaidis; H. H. Cudney

1995-01-01

474

Parallel evolutionary algorithms for optimization problems in aerospace engineering

This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of

J. F. Wang; J. Periaux; M. Sefrioui

2002-01-01

475

Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors

Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors Santosh Kumar Ten H. Lai Marc E,posner.1,sinha.43}@osu.edu Abstract-- The problem of sleep wakeup has been extensively studied the sleep- wakeup problem is NP-Hard for this model, several heuristics ex- ist. For the model of barrier

Sinha, Prasun

476

Allocating optimal index positions on tool magazines using genetic algorithms

This paper presents an optimisation system software developed for the determination of optimal index positions of cutting tools on the automatic tool changer (ATC) or turret magazine of CNC machine tools. Position selection is performed using a genetic algorithm (GA) which takes a list of cutting tools assigned to certain machining operations together with total number of index positions available

Türkay Dereli; I. Hüseyin Filiz

2000-01-01

477

A Niched Pareto Genetic Algorithm for Multiobjective Optimization

Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination

Jeffrey Horn; Nicholas Nafpliotis; David E. Goldberg

1994-01-01

478

THE ACO/F-RACE ALGORITHM FOR COMBINATORIAL OPTIMIZATION

optimization and on F-Race. The latter is a general method for the comparison of a number of candidates under uncertainty with the empirical estimation approach. F-Race [6, 5] is an algorithm for tuning metaheuristics.1 In the present paper, F-Race is used in an original way as a component of an ant

Libre de Bruxelles, UniversitÃ©

479

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION CARL OLSSON Faculty Vision Abstract Computer Vision is today a wide research area including topics like robot vision, image there has been a rapid development in understanding and modeling different computer vision applications

Lunds Universitet

480

An Optimal Algorithm for Intersecting Line Segments in the Plane

Themain contribution ofthiswork is an O(nlogr~ +k)-timeal gorithmfo rcomputingall k intersections among n line segments in the plane, This time complexity IS easdy shown to be optimal. Within thesame asymptotic cost, ouralgorithm canalso construct thesubdiwslon of theplancdefmed by the segments and compute which segment (if any) lies right above (or below) each intersection and each endpoint. The algorithm has been

Bernard Chazelle; Herbert Edelsbrunner

1988-01-01