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1

Hybrid genetic algorithm for electromagnetic topology optimization

This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on\\/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm

Chang-Hwan Im; Hyun-Kyo Jung; Yong-Joo Kim

2003-01-01

2

A hybrid algorithm for optimizing welding points of compliant assemblies

Purpose – The purpose of this paper is to propose a hybrid algorithm of the heuristic algorithm and the orthogonal design to optimize schemes of welding points (WPs). Assembly variation plays an important role in product manufacture. Different schemes of WPs can influence the sensitivity matrices between part and assembly variations. Design\\/methodology\\/approach – The paper proposes a hybrid algorithm to

Xing Yan-Feng

2009-01-01

3

Fuzzy multiple objective optimal system design by hybrid genetic algorithm

In this paper, we propose a method for solving fuzzy multiple objective optimal system design problems with GUB structure by hybridized genetic algorithms (HGA). This approach enables the flexible optimal system design by applying fuzzy goals and fuzzy constraints. In this genetic algorithm (GA), we propose the new chromosomes representation that represents the GUB structure simply and effectively at the

Masato Sasaki; Mitsuo Gen

2003-01-01

4

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

5

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

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

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

Kosmas, O T; Vlachos, D S; Simos, T E

2008-01-01

8

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

O. T. Kosmas; D. S. Vlachos

2009-05-04

9

New Hybrid Optimization Algorithms for Machine Scheduling Problems

is with the ISyE department, University of Wisconsin-Madison, Madison, WI. 53706 USA, and ... §III develops new hybrid algorithms. §IV establishes some properties of the algorithms. ...... When interpreted in the context of machine scheduling.

2006-12-03

10

Hybrid genetic algorithm research and its application in problem optimization

There is a lot of research in genetic algorithm about structural optimization. But as far as the large multi-goal program is concerned, it limits the application of genetic algorithm for the reason of its specialty and large calculation. In order to explore a new resolution, the author proposed a combining algorithm for structural optimization, which is based on genetic algorithm

Weijin Jiang I; Dingti Luol; Yusheng Xu; Xingming Sun

2004-01-01

11

This paper introduces a new hybrid algorithmic nature in- spired approach based on the concepts of the Honey Bees Mating Opti- mization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO

Yannis Marinakis; Magdalene Marinaki; Nikolaos F. Matsatsinis

2007-01-01

12

NASA Astrophysics Data System (ADS)

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO-LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO-LPM algorithm is also robust to random initial values.

Zhuang, Yufei; Huang, Haibin

2014-02-01

13

Hybrid genetic algorithm for optimization problems with permutation property

Permutation property has been recognized as a common but challenging feature in combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was

Hsiao-fan Wang; Kuang-yao Wu

2004-01-01

14

A new hybrid genetic algorithm for global optimization

In this paper a Hybrid Interval Genetic algorithm (HIG) is presented. The algorithm consists of two phases: In the first phase, interval arithmetic and especially an interval branch--and--bound algorithm is used to obtain small regions where candidate solutions lie. In this way, a population of potential solutions is initialized and initial bounds for the global minimum $f^*$ are obtained. In

D. G. Sotiropoulos; E. C. Stavropoulos; M. N. Vrahatis

1997-01-01

15

This paper introduces new hybrid cross-over methods and new hybrid selection methods for real coded genetic algorithm (RCGA), to solve the optimal control problem of a class of hybrid system, which is motivated by the structure of manufacturing environments that integrate process and optimal control. In this framework, the discrete entities have a state characterized by a temporal component whose

M. Senthil Arumugam; M. V. C. Rao; Ramaswamy Palaniappan

2005-01-01

16

A hybrid genetic algorithm for a class of global optimization problems with box constraints

In this paper, a new hybrid genetic algorithm is proposed, which combines the genetic algorithm with hill-climbing search steps differently from some former algorithms. The new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints. Finally, numerical examples show that this algorithm can yield the global optimum with high efficiency.

Quan Yuan; Zhiqing He; Huinan Leng

2008-01-01

17

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos

Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity\\u000a and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence,\\u000a slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm\\u000a (CGA)

Chun-Tian Cheng; Wen-Chuan Wang; Dong-Mei Xu; K. W. Chau

2008-01-01

18

A niche hybrid genetic algorithm for global optimization of continuous multimodal functions

A niche hybrid genetic algorithm (NHGA) is proposed in this paper to solve continuous multimodal optimization problems more efficiently, accurately and reliably. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder–Mead's simplex method into GAs. In the new architecture, the simplex search is first performed in the potential niches, which likely contain a

Lingyun Wei; Mei Zhao

2005-01-01

19

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

20

A hybrid genetic algorithm and bacterial foraging approach for global optimization

The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper pro- poses a hybrid approach involving genetic algorithms (GA) and bacterial foraging (BF) algorithms for function optimiza- tion problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover,

Dong Hwa Kim; Ajith Abraham; Jae Hoon Cho

2007-01-01

21

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques techniques and whose constraints are too complex for conventional genetic algorithm. The main idea is the han- dling of sub-domains of the CSP variables by the genetic algorithm. The population of the genetic

Paris-Sud XI, UniversitÃ© de

22

Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...

Zhang, Jiapu

2010-01-01

23

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

24

Quantum computing is applied to genetic algorithm (GA) to develop a class of quantum-inspired genetic algorithm (QGA) characterized by certain principles of quantum mechanisms for numerical optimization. Furthermore, a framework of hybrid QGA, named RQGA, is proposed by reasonably combining the Q-bit search of quantum algorithm in micro-space and classic genetic search of real-coded GA (RGA) in macro-space to achieve

Ling Wang; Fang Tang; Hao Wu

2005-01-01

25

Hybridizing Genetic Algorithms with Hill-Climbing Methods for Global Optimization: Two Possible Ways

Two methods of hybridizing genetic algorithms (GA) with hill-climbing for global optimization are investigated. The first one involves two interwoven levels of optimization-evolution (GA) and individual learning (hill-climbing)-which cooperate in the global optimization process. The second one consists of modifying a GA by the introduction of new genetic operators or by the alteration of traditional ones in such a way

Hugues Bersini; Jean-michel Renders

1994-01-01

26

Optimal Design of Two Stage to Orbit Spaceplane Using Hybrid Genetic Algorithm

NASA Astrophysics Data System (ADS)

This paper proposes a Hybrid Genetic Algorithm (Hybrid-GA) which is suitable for a large scale multidisciplinary optimization problem such as Two-Stage-To-Orbit (TSTO) spaceplane optimal design problem. The Hybrid-GA is implemented by combining Sequential Quadratic Programming (SQP) method with GA. When constructing the Hybrid-GA, there are three problems that should be solved; 1) decision of optimized variables and discrete method, 2) how to use results of local search, and 3) selection for survival method. These problems are discussed and solved in order to effectively combine SQP method with GA. In order to demonstrate the effectiveness of the proposed Hybrid-GA, the TSTO spaceplane optimal design problem, which consists of weight, aerodynamics, propulsion, and flight trajectory analyses, is investigated using the proposed Hybrid-GA, and the optimal results of Hybrid-GA are compared with that of Simple-GA, and SQP methods. Finally, strategy to achieve the TSTO spaceplane is proposed by comparing the optimal results of changing payload weight and maximum wing load factor of booster.

Imamura, Shunsuke; Kojima, Hirohisa; Tsuchiya, Takeshi; Kubota, Hirotoshi

27

A hybrid Honey Bees Mating Optimization algorithm for the Probabilistic Traveling Salesman Problem

The probabilistic traveling salesman problem is a variation of the classic traveling salesman problem and one of the most significant stochastic routing problems. In this paper, a new hybrid algorithmic nature inspired approach based on honey bees mating optimization (HBMO), greedy randomized adaptive search procedure (GRASP) and expanding neighborhood search strategy (ENS) is proposed for the solution of the probabilistic

Yannis Marinakis; Magdalene Marinaki

2009-01-01

28

Fuzzy nonlinear programming for mixed-discrete design optimization through hybrid genetic algorithm

Many practical engineering optimization problems involve discrete or integer design variables, and often the design decisions are to be made in a fuzzy environment in which the statements might be vague or imprecise. A mixed-discrete fuzzy nonlinear programming approach that combines the fuzzy ?-formulation with a hybrid genetic algorithm is proposed in this paper. This method can find a globally

Ying Xiong; Singiresu S. Rao

2004-01-01

29

A hybrid methodology is presented for the solution of the problem of the optimal allocation of reactive power sources. The technique is based upon a modified genetic algorithm, which is applied at an upper level stage, and a successive linear program at a lower level stage. The objective is the minimization of the total cost associated to the installation of

Alberto J. Urdaneta; Juan F. Gomez; Elmer Sorrentino; Luis Flores; Ricardo Diaz

1999-01-01

30

A hybrid genetic algorithm-neural network strategy for simulation optimization

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good

Ling Wang

2005-01-01

31

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

32

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

33

NASA Astrophysics Data System (ADS)

A hybrid genetic algorithm is proposed to optimal design the wavelength converter which using segmented grating structure and cascaded second-harmonic generation and difference-frequency generation process. Investigation of the influences of the structure parameters on conversion bandwidth and conversion response are carried out. High conversion efficiency, flat response and broad conversion bandwidth can be obtained simultaneously, by adding the segment number of QPM grating and optimizing the poling period of each segment. The utilizing of the hybrid genetic algorithm can not only make one obtain precise optimal results, but also shorten the simulation time significantly, so it is helpful to the practical design of the wavelength converters.

Liu, Lao; Cui, Jie

2014-08-01

34

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

35

Genetic algorithms (GA) have proven to be a useful method of optimization for difficult and discontinuous multidimensional engineering problems. A new method of optimization, particle swarm optimization (PSO), is able to accomplish the same goal as GA optimization in a new and faster way. The purpose of this paper is to investigate the foundations and performance of the two algorithms

Jacob Robinson; Seelig Sinton; Yahya Rahmat-Samii

2002-01-01

36

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

37

In this paper, we present an improved particle swarm optimization (PSO) algorithm for the hybrid flowshop scheduling (HFS) problem to minimize total weighted completion time. This problem has a strong practical background in process industry. For example, the integrated production process of steelmaking, continuous-casting, and hot rolling in the iron and steel industry, and the short-term scheduling problem of multistage

Lixin Tang; Xianpeng Wang

2010-01-01

38

Hybrid genetic algorithms and artificial neural networks for complex design optimization in CFD

The present paper is devoted to the study of design optimization strategies in the particular framework of complex computational fluid dynamics. Genetic algorithms are chosen as the optimization strategy, thanks to their robustness and flexibility. Two ways are explored to improve the behaviour of genetic algorithms in order to increase the efficiency of the search. First, approximated pre-evaluations based on

R. Duvigneau; M. Visonneau

2004-01-01

39

Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm

A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.

Chambers, D; Lehman, S; Dowla, F

2007-05-17

40

NASA Astrophysics Data System (ADS)

This paper presents a study of multi-objective optimal design of full state feedback controls. The goal of the design is to minimize several conflicting performance objective functions at the same time. The simple cell mapping method with a hybrid algorithm is used to find the multi-objective optimal design solutions. The multi-objective optimal design comes in a set of gains representing various compromises of the control system. Examples of regulation and tracking controls are presented to validate the control design.

Xiong, Fu-Rui; Qin, Zhi-Chang; Xue, Yang; Schütze, Oliver; Ding, Qian; Sun, Jian-Qiao

2014-05-01

41

Multiple sequence alignment (MSA) is one of the basic problems in computational biology. Realistic problem instances of MSA are computationally intractable for exact algorithms. One way to tackle MSA is to use Hidden Markov Models (HMMs), which are known to be very powerful in the related problem domain of speech recognition. However, the training of HMMs is computationally hard and there is no known exact method that can guarantee optimal training within reasonable computing time. Perhaps the most powerful training method is the Baum-Welch algorithm, which is fast, but bears the problem of stagnation at local optima. In the study reported in this paper, we used a hybrid algorithm combining particle swarm optimization with evolutionary algorithms to train HMMs for the alignment of protein sequences. Our experiments show that our approach yields better alignments for a set of benchmark protein sequences than the most commonly applied HMM training methods, such as Baum-Welch and Simulated Annealing. PMID:14642655

Rasmussen, Thomas Kiel; Krink, Thiemo

2003-11-01

42

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

43

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

44

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

45

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; Bahreininejad, Ardeshir

2011-08-01

46

A hybrid-algorithm-based parallel computing framework for optimal reservoir operation

NASA Astrophysics Data System (ADS)

Up to date, various optimization models have been developed to offer optimal operating policies for reservoirs. Each optimization model has its own merits and limitations, and no general algorithm exists even today. At times, some optimization models have to be combined to obtain desired results. In this paper, we present a parallel computing framework to combine various optimization models in a different way compared to traditional serial computing. This framework consists of three functional processor types, that is, master processor, slave processor and transfer processor. The master processor has a full computation scheme that allocates optimization models to slave processors; slave processors perform allocated optimization models; the transfer processor is in charge of the solution communication among all slave processors. Based on these, the proposed framework can perform various optimization models in parallel. Because of the solution communication, the framework can also integrate the merits of involved optimization models while in iteration and the performance of each optimization model can therefore be improved. And more, it can be concluded the framework can effectively improve the solution quality and increase the solution speed by making full use of computing power of parallel computers.

Li, X.; Wei, J.; Li, T.; Wang, G.

2012-12-01

47

Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. PMID:25462325

Lu, Shi Jing; Salleh, Abdul Hakim Mohamed; Mohamad, Mohd Saberi; Deris, Safaai; Omatu, Sigeru; Yoshioka, Michifumi

2014-09-28

48

Modeling Hybrid Genetic Algorithms

This paper looks at how one form of hybrid genetic algorithm can be modeledin the context of the existing models for the simple genetic algorithm; it shouldbe possible to model the integration of other types of local search with geneticalgorithms using the same basic approach. A secondary goal of this paper is toreview the existing models for finite and infinite

Darrell Whitley

1995-01-01

49

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

50

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

51

Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors. PMID:15607418

Li, Li; Jiang, Wei; Li, Xia; Moser, Kathy L; Guo, Zheng; Du, Lei; Wang, Qiuju; Topol, Eric J; Wang, Qing; Rao, Shaoqi

2005-01-01

52

NASA Astrophysics Data System (ADS)

This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.

Yang, Yun; Kimura, Shinji

53

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

54

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

55

A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems

A simple but effective evolutionary algorithm is proposed in this paper for solving complicated optimization problems. The new algorithm presents two hybridization operations incorporated with the conventional genetic ...

Xu, Y.G.

56

Hybrid genetic algorithms for analogue network synthesis

Network synthesis involves the selection of a suitable network topology and the choice of component values. Genetic algorithms can be used to perform both of these functions, but it is more efficient to adopt a hybrid approach in which a genetic algorithm is used to determine the network topology whilst the component values are obtained by numerical optimization

J. B. Grimbleby

1999-01-01

57

Optimal design of hybrid RO\\/MSF desalination plants Part I: Modeling and algorithms

Hybridization of seawater reverse osmosis (S WRO), desalting technology and the multi-stage flash (MSF) has been considered to improve the performance of the latter and reduce the cost of desalted water. Coupling of the two processes could be made on different levels of integration and the resulting water cost will depend on the selected configuration: not only the plant configuration

A. M. Helal; A. M. El-Nashar; E. Al-Katheeri; S. Al-Malek

2003-01-01

58

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

59

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

60

Cooperative evolutionary algorithm for space trajectory optimization

NASA Astrophysics Data System (ADS)

A hybrid evolutionary algorithm which synergistically exploits differential evolution, genetic algorithms and particle swarm optimization, has been developed and applied to spacecraft trajectory optimization. The cooperative procedure runs the three basic algorithms in parallel, while letting the best individuals migrate to the other populations at prescribed intervals. Rendezvous problems and round-trip Earth-Mars missions have been considered. The results show that the hybrid algorithm has better performance compared to the basic algorithms that are employed. In particular, for the rendezvous problem, a 100% efficiency can be obtained both by differential evolution and the genetic algorithm only when particular strategies and parameter settings are adopted. On the other hand, the hybrid algorithm always attains the global optimum, even though nonoptimal strategies and parameter settings are adopted. Also the number of function evaluations, which must be performed to attain the optimum, is reduced when the hybrid algorithm is used. In the case of Earth-Mars missions, the hybrid algorithm is successfully employed to determine mission opportunities in a large search space.

Rosa Sentinella, Matteo; Casalino, Lorenzo

2009-11-01

61

Stochastics and Statistics A hybrid hypercube Genetic algorithm approach for deploying many

Stochastics and Statistics A hybrid hypercube Â Genetic algorithm approach for deploying many Available online 9 September 2010 Keywords: Emergency response Hypercube Spatial queues Genetic algorithms), a location model and a metaheuristic optimization algorithm (genetic algorithm) for obtaining appropriate

ThÃ©venaz, Jacques

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

Aerodynamic Shape Optimization Using Hybridized Differential Evolution

NASA Technical Reports Server (NTRS)

An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.

Madavan, Nateri K.

2003-01-01

64

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

65

A hybrid genetic algorithm for synthesis of heat exchanger networks

A new hybrid genetic algorithm for optimal design of heat exchanger networks is developed. The mathematical model used in the algorithm is based on an explicit solution of stream temperatures of heat exchanger networks with the stage-wise superstructure. By taking heat transfer areas and heat capacity flow rates as genes in the genetic algorithm, the thermal performance and total cost

Xing Luo; Qing-Yun Wen; Georg Fieg

2009-01-01

66

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

67

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

68

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

69

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms #12;Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example #12;Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic

KjellstrÃ¶m, Hedvig

70

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic

KjellstrÃ¶m, Hedvig

71

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

72

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

73

Hybrid genetic algorithm based detection schemes for synchronous CDMA systems

We applied a hybrid genetic algorithm (GA) scheme as a suboptimal multiuser detection technique in bit-synchronous code division multiple access (CDMA) systems over a Gaussian channel as well as over a single-path Rayleigh fading channel. The proposed hybrid GA scheme attempts to search for the users' transmitted bit sequence that optimizes the correlation metric employed. Simulation results showed that the

K. Yen; L. Hanzo

2000-01-01

74

Parallel hybrid optimization methods for permutation based problems.

??Solving efficiently large benchmarks of NP-hard permutation-based problems requires the development of hybrid methods combining different classes of optimization algorithms. The key challenge here is… (more)

Mehdi, Malika

2011-01-01

75

Hybrid optimization for lithologic inversion and time-lapse monitoring using a binary formulation

Richard A. Krahenbuhl1 and Yaoguo Li1 ABSTRACT We have developed a hybrid optimization algorithm for bi specialized optimization algorithms. To meet this need, we develop a hybrid optimiza- tion algorithm by combining a genetic algorithm with quenched simulated annealing. The former allows for easy incorporation

76

Ant Algorithms for Discrete Optimization

Ant Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit#19;e, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization which took inspiration from the observation of ant colonies foraging behavior

Ducatelle, Frederick

77

Ant Algorithms for Discrete Optimization

Ant Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, UniversitÂ´e Libre, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization which took inspiration from the observation of ant colonies foraging behavior

Gambardella, Luca Maria

78

Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy. PMID:23892659

Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Illias, Rosli Md; Chong, Chuii Khim; Chai, Lian En

2014-03-01

79

Clustering Nominal and Numerical Data: A New Distance Concept for a Hybrid Genetic Algorithm

As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for clustering. CHyGA treats the clustering problem as an optimization problem and searches for an optimal number of clusters characterized by an optimal distribution of instances into

Laetitia Vermeulen-jourdan; Clarisse Dhaenens; El-ghazali Talbi

2004-01-01

80

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

81

Modeling Hybrid Genetic Algorithms Darrell Whitley

University, Fort Collins, CO 80523 whitley@cs.colostate.edu 1 INTRODUCTION A ``hybrid genetic algorithm algorithms is simple and straight forward. This paper also builds on earlier work by Whitley, Gordon

Whitley, Darrell

82

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

83

Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem

Chi-ming Lin; Mitsuo Gen

2008-01-01

84

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

85

A Self Adaptive Hybrid Genetic Algorithm

This paper presents a self-adaptive hybrid genetic algorithm (SAHGA) and compares its performance to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on two multi-modal test functions with complex geometry. The SAHGA is shown to be far more robust than the NAHGA, providing fast and reliable convergence across a broad range of parameter settings. For the

Felipe P. Espinoza; Barbara S. Minsker; David E. Goldberg

2000-01-01

86

By 2015, one million PHEVs are estimated to posses U.S. automotive market. Optimized management of PHEVs charging activities is necessary since growing penetration of PHEV fleet would place significant influences on grid, either by providing bulky energy storages or by requiring charging capacities. In this paper, dynamic programming (DP) technique is applied to seek minimum cost of PHEVs charging activities.

Zhihao Li; Alireza Khaligh; Navid Sabbaghi

2011-01-01

87

through a number of common multi-dimensional benchmark functions. Finally, a practical problem consisting design and optimization of an adaptive controller for a surge tank is simulated. The experimental results structures, and also developing intelligent control strategies like distributed and cooperative control [3

Zhang, Richard "Hao"

88

A new hybrid optimization method for loading pattern search

A new hybrid optimization method in reloading pattern search is presented in this paper, which mix genetic algorithm (GA) with tabu search (TS). The method combines global search of GA and local search of TS reasonably to enhance the search ability and computational efficiency. For verification and illustration of the advantage of this method, the proposed hybrid optimization method has been applied to the reactor reloading optimization calculation of Cartesian and hexagonal geometry core. The numerical results show that the hybrid method works faster and better than GA. (authors)

Tao, Wang [Shanghai Jiao Tong University, Shanghai 200030 (China); Zhongsheng, Xie [Xi'an Jiao Tong University, Xi'an 710049 (China)

2006-07-01

89

Ant algorithms for discrete optimization.

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic. PMID:10633574

Dorigo, M; Di Caro, G; Gambardella, L M

1999-01-01

90

The Rational Hybrid Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

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.

Clark, Michael

2006-12-01

91

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

92

A hybrid genetic algorithm for component sequencing and feeder arrangement

This paper presents a hybrid genetic algorithm to optimize the sequence of component placements on a printed circuit board and the arrangement of component types to feeders simultaneously for a pick-and-place machine with multiple stationary feeders, a fixed board table and a movable placement head. The objective of the problem is to minimize the total traveling distance, or the traveling

William Ho; Ping Ji

2004-01-01

93

A HYBRID GENETIC ALGORITHM APPROACH FOR OSPF WEIGHT SETTING PROBLEM

In this paper, a method based on hybrid genetic algorithm (GA), is presented to optimize administrative weights for OSPF routing. This method can be seen as an alternative to the local- search method in (1) or another GA-based method in (8,10). However, the GA as well as the objective function we use are different. Instead of minimizing a convex cost

Eueung Mulyana; Ulrich Killat

2002-01-01

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

NASA Astrophysics Data System (ADS)

A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.

Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.

2013-12-01

96

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

97

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

98

Design of doubly periodic filter and polarizer structures using a hybridized genetic algorithm

A hybrid genetic algorithm\\/local optimization technique for designing cascaded planar periodic structures such as frequency selective surfaces is developed. The genetic algorithm optimizes the shape and size of the periodic metallization in each of the screens as well as the thickness and dielectric constant of intervening dielectric layers in order to achieve a desired frequency response characteristic. Careful local optimization

D. S. Weile; E. Michielssen

1999-01-01

99

Exploring chemical space with discrete, gradient, and hybrid optimization methods.

Discrete, gradient, and hybrid optimization methods are applied to the challenge of discovering molecules with optimized properties. The cost and performance of the approaches were studied using a tight-binding model to maximize the static first electronic hyperpolarizability of molecules. Our analysis shows that discrete branch and bound methods provide robust strategies for inverse chemical design involving diverse chemical structures. Based on the linear combination of atomic potentials, a hybrid discrete-gradient optimization strategy significantly improves the performance of the gradient methods. The hybrid method performs better than dead-end elimination and competes with branch and bound and genetic algorithms. The branch and bound methods for these model Hamiltonians are more cost effective than genetic algorithms for moderate-sized molecular optimization. PMID:19045331

Balamurugan, D; Yang, Weitao; Beratan, David N

2008-11-01

100

A Hybrid Genetic Algorithm for School Timetabling

Hybrid Genetic Algorithms apply so called hybrid or repair operators or include problem specific knowledge about the problem domain in their mutation and crossover operators. These operators use local search to repair or avoid illegal or unsuitable assignments or just to improve the quality of the solutions already found.

Peter Wilke; Matthias Gröbner; Norbert Oster

2002-01-01

101

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

102

The theory of hybrid stochastic algorithms

These lectures introduce the family of Hybrid Stochastic Algorithms for performing Monte Carlo calculations in Quantum Field Theory. After explaining the basic concepts of Monte Carlo integration we discuss the properties of Markov processes and one particularly useful example of them: the Metropolis algorithm. Building upon this framework we consider the Hybrid and Langevin algorithms from the viewpoint that they are approximate versions of the Hybrid Monte Carlo method; and thus we are led to consider Molecular Dynamics using the Leapfrog algorithm. The lectures conclude by reviewing recent progress in these areas, explaining higher-order integration schemes, the asymptotic large-volume behaviour of the various algorithms, and some simple exact results obtained by applying them to free field theory. It is attempted throughout to give simple yet correct proofs of the various results encountered. 38 refs.

Kennedy, A.D. (Florida State Univ., Tallahassee, FL (USA). Supercomputer Computations Research Inst.)

1989-11-21

103

Economic Dispatch Using Genetic Algorithm Based Hybrid Approach

Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)

Tahir Nadeem Malik; Aftab Ahmad [University of Engineering and Technology, Taxila (Pakistan); Shahab Khushnood [National Power Construction Corporation - NPCC, 9-Shadman II, Lahore -54000 (Pakistan)

2006-07-01

104

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

105

Short-Term Load Forecasting Using Bayesian Neural Networks Learned by Hybrid Monte Carlo Algorithm

This paper develops a Bayesian technique to design an optimal neural network model for short term load forecasting. We use Hybrid Monte Carlo algorithm as a learning scheme to yield the weight vector of Bayesian neural network. In Hybrid Monte Carlo learning algorithm, the Bayesian neural network is considered as a special Hamiltonian dynamical system. The Hamilton function is precisely

Dong-xiao Niu; Hui-feng Shi; Desheng Dash Wu

106

Hybrid optimization schemes for simulation-based problems.

The inclusion of computer simulations in the study and design of complex engineering systems has created a need for efficient approaches to simulation-based optimization. For example, in water resources management problems, optimization problems regularly consist of objective functions and constraints that rely on output from a PDE-based simulator. Various assumptions can be made to simplify either the objective function or the physical system so that gradient-based methods apply, however the incorporation of realistic objection functions can be accomplished given the availability of derivative-free optimization methods. A wide variety of derivative-free methods exist and each method has both advantages and disadvantages. Therefore, to address such problems, we propose a hybrid approach, which allows the combining of beneficial elements of multiple methods in order to more efficiently search the design space. Specifically, in this paper, we illustrate the capabilities of two novel algorithms; one which hybridizes pattern search optimization with Gaussian Process emulation and the other which hybridizes pattern search and a genetic algorithm. We describe the hybrid methods and give some numerical results for a hydrological application which illustrate that the hybrids find an optimal solution under conditions for which traditional optimal search methods fail.

Fowler, Katie (Clarkson University, NY); Gray, Genetha Anne; Griffin, Joshua D. (SAS Institute, NC)

2010-05-01

107

Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in a Multi-Processor Environment

Scheduling partially ordered tasks in a multiple-processor environment is a very complex combinatorial optimization problem. In this paper, hybrid genetic algorithms for the scheduling optimization problem are presented. We first present a non-string representation of the solutions for scheduling problems. Then we provide a hybrid mechanism for the choice of genetic operators. The issue of illegal solution is addressed as

Man Lin; Laurence Tianruo Yang

1999-01-01

108

An architecture for adaptive algorithmic hybrids.

We describe a cognitive architecture for creating more robust intelligent systems. Our approach is to enable hybrids of algorithms based on different computational formalisms to be executed. The architecture is motivated by some features of human cognitive architecture and the following beliefs: 1) Most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and 2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. The key to this approach is the following two principles: 1) Algorithms based on very different computational frameworks (e.g., logical reasoning, probabilistic inference, and case-based reasoning) can be implemented using the same set of five common functions and 2) each of these common functions can be executed using multiple data structures and algorithms. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme in planning, spatial reasoning, robotics, and information retrieval illustrate that this approach to hybridizing algorithms enables qualitative and measurable quantitative advances in the abilities of intelligent systems. PMID:19914898

Cassimatis, Nicholas; Bignoli, Perrin; Bugajska, Magdalena; Dugas, Scott; Kurup, Unmesh; Murugesan, Arthi; Bello, Paul

2010-06-01

109

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

110

A locally optimal handoff algorithm

The design of handoff algorithms for cellular communication systems based on mobile signal strength measurements is considered. The design problem is posed as an optimization to obtain the best tradeoff between expected number of service failures and expected number of handoffs, where a service failure is defined to be the event that the signal strength falls below a level required

O. E. Kelly; V. V. Veeravalli

1995-01-01

111

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

112

Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

This paper demonstrates how genetic algorithms can be used to discover the structureof a Bayesian network from a given database with cases. The results presented, were obtained byapplying four different types of genetic algorithms -- SSGA (Steady State Genetic Algorithm), GAe(Genetic Algorithm elistist of degree ), hSSGA (hybrid Steady State Genetic Algorithm) and thehGAe (hybrid Genetic Algorithm elitist of degree

Pedro Larrañaga; Roberto Murga; Mikel Poza; Cindy Kuijpers

1995-01-01

113

Design of optimal hybrid form FIR filter

This paper examines the problem of designing the opti- mal hybrid form FIR filter subjected to a minimum cycle- time constraint. We formulate the problem as one of de- termining the optimal partitioning of the hybrid form FIR filter into subsections. Each subsection can be optimized independently using other methods. We then show how the problem can be solved efficientlyusing

Kei-yong Khoo; Zhan Yu; Alan N. Willson Jr.

2001-01-01

114

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

115

A Novel Hybrid Self-Adaptive Bat Algorithm

Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction. PMID:25187904

Fister, Iztok; Brest, Janez

2014-01-01

116

Hybrid optimization for a binary inverse problem Richard A. Krahenbuhl* and Yaoguo Li

a hybrid optimization algorithm for inversion of gravity data using a binary formulation. The new algorithm utilizes the Genetic Algorithm (GA) as a global search tool, while implementing Quenched Simulated techniques for carrying out such discrete-variable minimization problems, namely, genetic algorithm (GA

117

Hybrid functions approach for nonlinear constrained optimal control problems

NASA Astrophysics Data System (ADS)

In this paper, a new numerical method for solving the nonlinear constrained optimal control with quadratic performance index is presented. The method is based upon hybrid functions approximation. The properties of hybrid functions consisting of block-pulse functions and Bernoulli polynomials are presented. The operational matrix of integration is introduced. This matrix is then utilized to reduce the solution of the nonlinear constrained optimal control to a nonlinear programming one to which existing well-developed algorithms may be applied. Illustrative examples are included to demonstrate the validity and applicability of the technique.

Mashayekhi, S.; Ordokhani, Y.; Razzaghi, M.

2012-04-01

118

Bicriteria transportation problem by hybrid genetic algorithm

In this paper, we present a hybrid genetic algorithm to solve the bicriteria transportation problem. we absorb the concept on spanning tree and adopt the Prüfer number as it is capable of equally and uniquely representing all possible basic solutions. We designed the criterion which chromosomes can be always feasibly converted to a transportation tree. In order to improve the

Mitsuo Gen; Kenichi Ida; Yinzhen Li

1998-01-01

119

A hybrid genetic algorithm for the open shop scheduling problem

This paper examines the development and application of a hybrid genetic algorithm (HGA) to the open shop scheduling problem. The hybrid algorithm incorporates a local improvement procedure based on tabu search (TS) into a basic genetic algorithm (GA). The incorporation of the local improvement procedure enables the algorithm to perform genetic search over the subspace of local optima. The algorithm

Ching-Fang Liaw

2000-01-01

120

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

121

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

122

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

123

--Electrically small antennas, genetic algorithm (GA), metamaterials, optimization methods. I. INTRODUCTION ANTENNASIEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 55, NO. 3, MARCH 2007 731 A Hybrid Optimization-MATLAB based hybrid optimization model. The optimized-analytical model is specifically applied to a spherical

Ziolkowski, Richard W.

124

Hybrid intelligent algorithms for industrial production planning

NASA Astrophysics Data System (ADS)

In this paper, the main significant contributions of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in the industrial production planning problems has been investigated thoroughly. This non-linear membership function is flexible and convenience to the decision makers in their decision making process. Secondly, a nonlinear objective function in the form of cubic function for fuzzy optimization problems is successfully solved by 15 hybrid and non-hybrid optimization techniques from the area of soft computing and classical approaches. An intelligent performance analysis table is tabulated to the convenience of decision makers and implementers to select the niche optimization techniques to apply in real word problem solving approach particularly related to industrial engineering problems.

Vasant, P.

2012-11-01

125

New Hybrid Genetic Algorithms for the Frequency Assignment Problem

This paper presents a new hybrid genetic algorithm used to solve a frequency assignment problem. The hybrid genetic algorithm presented in this paper uses two original mutation operators. The first mutation operator is based on a greedy algorithm and the second one on an original probabilistic tabu search. The results obtained by our algorithm are better than the best known

Miguel Alabau; Lhassane Idoumghar; René Schott

2001-01-01

126

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

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

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

2009-11-01

127

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

128

Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509

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

2013-09-01

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

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

131

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

132

The RHSA strategy for the allocation of outbound containers based on the hybrid genetic algorithm

NASA Astrophysics Data System (ADS)

Secure storage yard is one of the optimal core goals of container transportation; thus, making the necessary storage arrangements has become the most crucial part of the container terminal management systems (CTMS). This paper investigates a random hybrid stacking algorithm (RHSA) for outbound containers that randomly enter the yard. In the first stage of RHSA, the distribution among blocks was analyzed with respect to the utilization ratio. In the second stage, the optimization of bay configuration was carried out by using the hybrid genetic algorithm. Moreover, an experiment was performed to test the RHSA. The results show that the explored algorithm is useful to increase the efficiency.

Le, Meilong; Yu, Hang

2013-09-01

133

A hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines

In this paper, I propose a hybrid genetic algorithm (HGAV) incorporating a greedy interchange local optimization algorithm for the vehicle scheduling problem with service due times and time deadlines where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicles are allowed to visit the nodes exceeding their

Yang-Byung Park

2001-01-01

134

Hybrid Particle Swarm Optimization for Vehicle Routing Problem with Reverse Logistics

The vehicle routing problem (VRP) is a well-known combinatorial optimization problem, holds a central place in logistics management. This paper proposes an hybrid particle swarm optimization (PSO) for VRP with reverse logistics, which possesses a new strategy to represent the solution of the problem, and in the evolution of PSO, SA algorithm is used to optimize the sequence of the

Yang Peng

2009-01-01

135

Flexible optimization of text recognition algorithms

This paper presents a system for the optimization of text recognition algorithms. First a theoretic four-staged model of text recognition is proposed. In this four-staged model, the second stage called text localization is optimized. A reinterpreted version of the F measure is used as a fitness indicator for optimization of the localization. The optimization method is described and the role

Britta Meixner; Florian Pein; Harald Kosch

2010-01-01

136

NASA Astrophysics Data System (ADS)

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; Wang, Jun; Zhou, Shudao; Zhou, Bihua

2014-03-01

137

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

138

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

139

A hybrid genetic algorithm for feature selection wrapper based on mutual information

In this study, a hybrid genetic algorithm is adopted to find a subset of features that are most relevant to the classification task. Two stages of optimization are involved. The outer optimization stage completes the global search for the best subset of features in a wrapper way, in which the mutual information between the predictive labels of a trained classifier

Jinjie Huang; Yunze Cai; Xiaoming Xu

2007-01-01

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

Genetic symbiosis algorithm for multiobjective optimization problem

Evolutionary algorithms are often well-suited for optimization problems. Since the mid-1980's, interest in multiobjective problems has been expanding rapidly. Various evolutionary algorithms have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we proposed a genetic symbiosis algorithm (GSA) for multi-object optimization problems (MOP) based on the symbiotic concept found widely

Jiangming Mao; K. Hirasawa; Jinlu Hu; J. Murata

2000-01-01

142

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

143

NASA Astrophysics Data System (ADS)

This paper proposes a new hybrid algorithm combining harmony search (HS) algorithm and interior point method (IPM) for economic dispatch (ED) problem with valve-point effect. ED problem with valve-point effect is modeled as a non-linear, constrained and non-convex optimization problem having several local minima. IPM is a best non-linear optimization method for convex optimization problems. Since ED problem with valve-point effect has multiple local minima, IPM results in a local optimum solution. In order to avoid IPM getting trapped in a local optimum, a new evolutionary algorithm HS, which is good in global exploration, has been combined. In the hybrid method, HS is used for global search and IPM for local search. The hybrid method has been tested on three different test systems to prove its effectiveness. Finally, the simulation results are also compared with other methods reported in the literature.

Sivasubramani, S.; Ahmad, Md. Samar

2014-06-01

144

Generation of Compliant Mechanisms using Hybrid Genetic Algorithm

NASA Astrophysics Data System (ADS)

Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( ?). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for ? value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.

Sharma, D.; Deb, K.

2014-10-01

145

Multiple agent hybrid control: carrier manifolds and chattering approximations to optimal control

Developing complete foundations for multiple-agent hybrid control is an arduous task and also requires important mathematical design choices. It involves synthesizing in a coherent fashion theorems and algorithms from a variety of fields. We discuss the notion of carrier manifold and of chattering approximations to optimal controls on carrier manifolds. The new idea we introduce here, beyond the hybrid systems

W. Kohn; A. Nerode; J. B. Remmel; Xiolin Ge

1994-01-01

146

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

147

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

148

Linear array synthesis using a hybrid genetic algorithm

There has been some interest in the application of genetic algorithms to antenna array synthesis problems. A direct encoding hybrid genetic algorithm is applied to a variable length and fixed number of elements linear array synthesis problem. A comparison is made with published results. The algorithm proposed paper yields substantially better results than the previously published work. The algorithm utilizes

Michael J. Buckley

1996-01-01

149

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

150

A multiscale hybrid algorithm for fluctuating hydrodynamics

NASA Astrophysics Data System (ADS)

We develop an algorithmic hybrid for simulating multiscale fluid flow with microscopic fluctuations. Random fluctuations occur in fluids at microscopic scales, and these microscopic fluctuations can lead to macroscopic system effects. For example, in the Rayleigh-Taylor problem, where a relatively heavy gas sits on top of a relatively light gas, spontaneous microscopic fluctuation at the interface of the gases leads to turbulent mixing. Given near-term computational power, the physical and temporal domain on which these systems can be studied using traditional particle simulations is extremely limited. Therefore, we seek algorithmic solutions to increase the effective computing power available to study such problems. We develop an explicit numerical solver for the Landau-Lifshitz Navier-Stokes (LLNS) equations, which incorporate thermal fluctuations into macroscopic hydrodynamics via stochastic; fluxes. A major goal is to correctly preserve the influence of the microscopic fluctuations on the behavior of the system. We show that several classical approaches fail to accurately reproduce fluctuations in energy or density, and we introduce a customized conservative centered scheme with a third-order Runge-Kutta temporal integrator that is specficially designed to produce correct fluctuations in all conserved quantities. We then use the adaptive mesh and algorithm refinement (AMAR) paradigm to create a multiscale hybrid method by coupling our LLNS solver with the direct simulation Monte Carlo (DSMC) particle method. We present numerical tests of systems in and out of equilibrium, including time-dependent systems, and demonstrate dynamic adaptive refinement. Mean system behavior and second moment statistics of our simulations match theoretical values and benchmarks well. We find that particular attention should be paid to the spectrum of the flux at the interface between the particle and continuum methods, specifically at non-hydrodynamic time scales. As an extension of the basic hybrid method, we consider the dynamics of a binary mixture of gases. Benchmark test problems, including a system under concentration gradient, match theory and molecular simulation well. We find that to study mixtures of gases with unequal masses, at equilibrium it may be reasonable to neglect the Soret effect; however, inclusion of the baro-diffusion effect is important.

Williams, Sarah Anne

151

Stochastic optimization algorithms for barrier dividend strategies

NASA Astrophysics Data System (ADS)

This work focuses on finding optimal barrier policy for an insurance risk model when the dividends are paid to the share holders according to a barrier strategy. A new approach based on stochastic optimization methods is developed. Compared with the existing results in the literature, more general surplus processes are considered. Precise models of the surplus need not be known; only noise-corrupted observations of the dividends are used. Using barrier-type strategies, a class of stochastic optimization algorithms are developed. Convergence of the algorithm is analyzed; rate of convergence is also provided. Numerical results are reported to demonstrate the performance of the algorithm.

Yin, G.; Song, Q. S.; Yang, H.

2009-01-01

152

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

153

An efficient hybrid genetic algorithm (HGA) approach for solving the economic dispatch problem (EDP) with valve-point effect is presented in this paper. The proposed method combines the GA algorithm with the differential evolution (DE) and sequential quadratic programming (SQP) technique to improve the performance of the algorithm. GA is the main optimizer, while the DE and SQP are used to

Dakuo He; Fuli Wang; Zhizhong Mao

2008-01-01

154

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

155

Restarted local search algorithms for continuous black box optimization.

Several local search algorithms for real-valued domains (axis parallel line search, Nelder-Mead simplex search, Rosenbrock's algorithm, quasi-Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi-start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard-to-beat baseline algorithm), (2) to describe individual features of these algorithms and show how they influence the algorithm on different problems, and (3) to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions, it is better to choose an algorithm based on quadratic modeling, such as NEWUOA or a quasi-Newton method. PMID:22779407

Pošík, Petr; Huyer, Waltraud

2012-01-01

156

Aerodynamic Shape Optimization using an Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.

Hoist, Terry L.; Pulliam, Thomas H.

2003-01-01

157

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 writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high-performance computers such as the Cray Y-MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high-rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35-story tower (with 1,262 elements and 936 degrees of freedom), using eight processors. 9 refs.

Adeli, H.; Cheng, N. (Ohio State Univ., Columbus, OH (United States))

1994-07-01

158

Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine

By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency. PMID:24294135

Yu, Zhang; Yang, Xiaomei

2013-01-01

159

Ant Algorithms Solve Difficult Optimization Problems

Ant Algorithms Solve Difficult Optimization Problems Marco Dorigo IRIDIA UniversitÂ´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, UniversitÃ©

160

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

161

Optimization Based Image Segmentation by Genetic Algorithms

Optimization Based Image Segmentation by Genetic Algorithms S. Chabrier1 , C. Rosenberger2 , B them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation cri- terion which quantifies the quality of an image segmentation result

Paris-Sud XI, UniversitÃ© de

162

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

163

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

164

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

165

Finding balanced graph bi-partitions using a hybrid genetic algorithm

Proposes a hybrid genetic algorithm (GA) for the graph-balanced bi-partition problem, a challenging NP-hard combinatorial optimization problem arising in many practical applications. The hybrid character of the GA lies in the application of a heuristic procedure to improve candidate solutions. The basic idea behind our heuristic is to identify and exploit clusters, i.e. subgraphs with a relatively high edge density.

A. G. Steenbeek; E. Marchiori; A. E. Eiben

1998-01-01

166

Spacecraft long-duration phasing maneuver optimization using hybrid approach

NASA Astrophysics Data System (ADS)

Before a manned orbital rendezvous mission, the target spacecraft usually performs several maneuvers to adjust the initial phase angle of the orbital rendezvous and to coordinate the injection of the chaser. This maneuvering process is referred to as the "target phasing mission". This target phasing presents an orbital long-duration two-point boundary value problem. Further, when the maneuver revolution numbers are used as design variables, the target phasing maneuver's optimization becomes a mixed integer nonlinear programming problem. This paper presents a new optimization method for this phasing maneuver mission, employing a hybrid approach. First, we provide an approximate phasing optimization problem that considers the phase angle influences of node drift and orbital altitude decay. This problem is then optimized using a hybrid approach that integrates branch-and-bound and sequential quadratic programming. Second, a shooting iteration method is adopted to improve the solution to the approximate problem in order to satisfy the terminal constraints of high-precision numerical integration. The proposed method is then applied to an operational target phasing maneuver problem. The results lead to four major conclusions: (1) The proposed approximate phasing optimization model presents a good approximation of the operational mission. (2) The hybrid optimization approach can solve the approximate problem effectively, and the shooting iteration used to arrive at a high-precision solution converges steadily and rapidly. (3) Compared with mixed-code genetic algorithm, the proposed method can obtain a similar result with a lower computation cost and, compared with the approximate model that does not consider node drift and orbital altitude decay, the proposed method has better convergence efficiency. (4) The terminal time of target phasing remains almost constant when the initial semi-major axis increases in a limited interval, and the transition appears only when there is a change in the terminal revolution number.

Zhang, Jin; Wang, Xiang; Ma, Xiao-bing; Tang, Yi; Huang, Hai-bing

2012-03-01

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

An algorithm for online optimization of accelerators

NASA Astrophysics Data System (ADS)

A general algorithm is developed for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. It is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.

Huang, Xiaobiao; Corbett, Jeff; Safranek, James; Wu, Juhao

2013-10-01

169

Testing a Fourier Accelerated Hybrid Monte Carlo Algorithm

We describe a Fourier Accelerated Hybrid Monte Carlo algorithm suitable for dynamical fermion simulations of non-gauge models. We test the algorithm in supersymmetric quantum mechanics viewed as a one-dimensional Euclidean lattice field theory. We find dramatic reductions in the autocorrelation time of the algorithm in comparison to standard HMC.

S. Catterall; S. Karamov

2001-12-17

170

The production ofbiodiesel by transesterification of waste cooking oil (WCO) to partially substitute petroleum diesel is one of the measures for solving the twin problems of environment pollution and energy demand. An environmentally benign process for the enzymatic transesterification using immobilized lipase has attracted considerable attention for biodiesel production. Here, a superparamagnetic, high surface area substrate for lipase immobilization is evaluated. These immobilization substrates are composed of mesoporous silica/superparamagnetic iron oxide core-shell nanoparticles. The effects of methanol ratio to WCO, lipase concentration, water content and reaction time on the synthesis of biodiesel were analysed by utilizing the response surface methodology (RSM). A quadratic response surface equation for calculating fatty acid methyl ester (FAME) content as the objective function was established based on experimental data obtained in accordance with the central composite design. The RSM-based model was then used as the fitness function for genetic algorithm (GA) to optimize its input space. Hybrid RSM-GA predicted the maximum FAME content (91%) at the optimum level of medium variables: methanol ratio to WCO, 4.34; lipase content, 43.6%; water content, 10.22%; and reaction time, 6h. Moreover, the immobilized lipase could be used for four times without considerable loss of the activity. PMID:24350474

Karimi, Mahmoud; Keyhani, Alireza; Akram, Asadolah; Rahman, Masoud; Jenkins, Bryan; Stroeve, Pieter

2013-01-01

171

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

172

A HYBRID GENETIC ALGORITHM FOR MULTIOBJECTIVE PROBLEMS WITH ACTIVITY ANALYSIS-BASED LOCAL SEARCH

Technology Transfer Automated Retrieval System (TEKTRAN)

The objective of this research was the development of a method that integrated a data envelopment analysis economic model of production with a biophysical model, with optimization over multiple objectives. We specified a hybrid genetic algorithm using DEA as a local search method, and NSGA-II for c...

173

Hybrid genetic algorithm for economic dispatch with valve-point effect

This paper presents an efficient method for solving the economic dispatch problem (EDP) through combination of genetic algorithm (GA), the sequential quadratic programming (SQP) technique, uniform design technique, the maximum entropy principle, simplex crossover and non-uniform mutation. The proposed hybrid technique uses GA as the main optimizer, the SQP to fine tune in the solution of the GA run. Based

Da-kuo He; Fu-li Wang; Zhi-zhong Mao

2008-01-01

174

A parallel hybrid genetic algorithm for protein structure prediction on the computational grid

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the problem using the computational grid. The use of a near-optimal metaheuristic, such as a GA, allows a significant reduction in the number of explored potential structures. However, the

Alexandru-adrian Tantar; Nouredine Melab; El-ghazali Talbi; Benjamin Parent; Dragos Horvath

2007-01-01

175

An improved hybrid genetic algorithm: new results for the quadratic assignment problem

In this paper, we propose an improved hybrid genetic algorithm (IHGA). It uses a robust local improvement procedure as well as an effective restart mechanism that is based on so-called ‘shift mutations’. IHGA has been applied to the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). The results obtained from the experiments on different QAP instances show that the

Alfonsas Misevicius

2004-01-01

176

Component scheduling for chip shooter machines: a hybrid genetic algorithm approach

A chip shooter machine for electronic component assembly has a movable feeder carrier, a movable X–Y table carrying a printed circuit board (PCB), and a rotary turret with multiple assembly heads. This paper presents a hybrid genetic algorithm (HGA) to optimize the sequence of component placements and the arrangement of component types to feeders simultaneously for a chip shooter machine,

William Ho; Ping Ji

2003-01-01

177

Hybrid Monte Carlo algorithm for lattice QCD with two flavors of dynamical Ginsparg-Wilson quarks

We study aspects concerning numerical simulations of Lattice QCD with two flavors of dynamical Ginsparg-Wilson quarks with degenerate masses. A Hybrid Monte Carlo algorithm is described and the formula for the fermionic force is derived for two specific implementations. The implementation with optimal rational approximation method is favored both in CPU time and memory consumption.

Chuan Liu

1998-11-04

178

A hybrid algorithm for far-field noise minimization Markus P. Rumpfkeil *, David W. Zingg

trailing edge airfoil in an unsteady turbulent flow environment. The examples pre- sented demonstrateA hybrid algorithm for far-field noise minimization Markus P. Rumpfkeil *, David W. Zingg Available online xxxx Keywords: Unsteady adjoint Unsteady optimization Noise prediction Far-field noise

Zingg, David W.

179

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

180

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

181

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

182

Desktop and HIL Validation of Hybrid-Electric-Vehicle Battery-Management-System Algorithms

The battery management system (BMS) of a hybrid- electric-vehicle (HEV) battery pack comprises hardware and software to monitor pack status and optimize performance. One of its important functions is to execute algorithms that continuously estimate battery state-of-charge (SOC), state-of-health (SOH), and available power. The primary difficulty when validating these algorithms is that there are no sensors that can measure SOC,

Gregory L. Plett; Robert Billings; Martin J. Klein

183

Algorithm selection in structural optimization

Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit ...

Clune, Rory P. (Rory Patrick)

2013-01-01

184

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

185

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

186

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

187

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

188

EFFICIENT SOLUTION OF OPTIMAL CONTROL PROBLEMS USING HYBRID SYSTEMS

the synthesis of optimal controls for continuous feedback systems by recasting the problem to a hybrid optimal of a computationally appealing technique for synthesizing optimal controls for continuous feedback systems x = f(x, u is minimized, for each initial condition in a specified set Rn . Casting the problem as a hybrid control

Broucke, Mireille E.

189

In this survey we discuss different state-of-the-art approaches of combining exact algorithms and metaheuristics to solve combinatorial optimization problems. Some of these hybrids mainly aim at providing optimal solutions in shorter time, while others primarily focus on getting better heuristic solutions. The two main categories in which we divide the approaches are collaborative versus integrative combinations. We further classify the

Jakob Puchinger; Günther R. Raidl

2005-01-01

190

A hybrid of the genetic algorithm and concurrent simplex

A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis by DAVID ETHAN RANDOLPH Submitted to the Office of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1995 Major Subject: Computer Science A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis DAVID ETHAN RANDOLPH Submitted to Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE...

Randolph, David Ethan

1995-01-01

191

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments. PMID:20064758

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

192

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

193

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

194

Power economic dispatch using a hybrid genetic algorithm

This letter outlines a hybrid genetic algorithm (HGA) for solving the economic dispatch problem. The algorithm incorporates the solution produced by an improved Hopfield neural network (NN) as a part of its initial population. Elitism, arithmetic crossover and mutation are used in the GAs to generate successive sets of possible operating policies. The technique improves the quality of the solution

T. Yalcinoz; H. Altun

2001-01-01

195

A hybrid genetic algorithm for the channel routing problem

We present a Hybrid Genetic Algorithm (HGA) for the Channel Routing Problem (CRP). To do so we combine a Genetic Algorithm (GA) with domain specific knowledge, i.e. the genetic operators make use of the rip-up and reroute technique. Thereby the execution time of our method is faster than previously presented evolutionary based approaches. Furthermore, concerning space complexity we show by

Nicole Göckel; Gregor Pudelko; Rolf Drechsler; Bernd Becker

1996-01-01

196

A hybrid genetic algorithm for the job shop scheduling problem

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local

José Fernando Gonçalves; Jorge José De Magalhães Mendes; Maur??cio G. C. Resende

2005-01-01

197

Path Cost Optimization Using Genetic Algorithm with Supervised Crossover Operator

Path Cost Optimization Using Genetic Algorithm with Supervised Crossover Operator Chi-Tsun Cheng of these algorithms rise dras- tically. Meta-heuristic algorithms such as evolutionary al- gorithms and genetic-based path cost optimization algorithm is proposed. The generic crossover operator in genetic algorithms

Tse, Chi K. "Michael"

198

Optimal Monte Carlo Algorithms Ivan T. Dimov

Optimal Monte Carlo Algorithms Ivan T. Dimov Institute for Parallel Processing Department Centre University of Reading Whiteknights, PO Box 217, Reading, RG6 6AH, UK E-mail: I.T.Dimov@reading.ac.uk; ivdimov@bas.bg Web site: http://www.personal.rdg.ac.uk/ sis04itd/ Abstract The question "what Monte Carlo

Dimov, Ivan

199

Groundwater Remediation Strategy Using Global Optimization Algorithms

Groundwater Remediation Strategy Using Global Optimization Algorithms Shreedhar Maskey1 ; Andreja Jonoski2 ; and Dimitri P. Solomatine3 Abstract: The remediation of groundwater contamination by pumping as decision variables. Groundwater flow and particle-tracking models MODFLOW and MODPATH and a GO tool GLOBE

Neumaier, Arnold

200

A genetic algorithm for fin profile optimization

In the present work a genetic algorithm is proposed in order to optimize the thermal performances of finned surfaces. The bidimensional temperature distribution on the longitudinal section of the fin is calculated by resorting to the finite elements method. The heat flux dissipated by a generic profile fin is compared with the heat flux removed by the rectangular profile fin

Giampietro Fabbri

1997-01-01

201

Genetic Algorithms for Real Parameter Optimization

This paper is concerned with the application of gen etic algorithms to optimization problems over several real parameters. It is shown that k-point crossover (for k small relative to the number of parameters) can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters. Mutation can also be co nsidered as a

Alden H. Wright

1991-01-01

202

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

203

Interior search algorithm (ISA): a novel approach for global optimization.

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. PMID:24785823

Gandomi, Amir H

2014-07-01

204

\\u000a In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the permutation flowshop sequencing problem with the makespan criterion. A new crossover\\u000a operator, here we call it the PTL crossover operator, is presented. In addition, the DPSO algorithm is hybridized with a simple local search algorithm based on an insert neighborhood to further improve the solution

Quan-Ke Pan; M. Fatih Tasgetiren; Yun-Chia Liang

2007-01-01

205

NASA Astrophysics Data System (ADS)

This paper combines a verified interval optimization method with the FEM for designing structures, which is denominated as the Hybrid Interval Genetic Algorithm (HIGA). This algorithm can neglect formulated equations and interval analysis, and while determining the optimum interval parameters. Furthermore, it can also maximize the design scope. In this paper, this algorithm is implemented for both a truss and frame structure. The interval optimizations include the static and dynamic responses of these structures. The results show that the algorithm which combines the IGA with the FEM can determine the feasible interval design parameters of structures with allowable objective errors.

Shiau, Ting-Nung; Kang, Chung-Hao; Liu, De-Shin; Hsu, Wei-Chun

206

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

207

A Hybrid Monkey Search Algorithm for Clustering Analysis

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. PMID:24772039

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

208

A hybrid algorithm with GA and DAEM

NASA Astrophysics Data System (ADS)

Although the expectation-maximization (EM) algorithm has been widely used for finding maximum likelihood estimation of parameters in probabilistic models, it has the problem of trapping by local maxima. To overcome this problem, the deterministic annealing EM (DAEM) algorithm was once proposed and had achieved better performance than EM algorithm, but it is not very effective at avoiding local maxima. In this paper, a solution is proposed by integrating GA and DAEM into one procedure to further improve the solution quality. The population based search of genetic algorithm will produce different solutions and thus can increase the search space of DAEM. Therefore, the proposed algorithm will reach better solution than just using DAEM. The algorithm retains the property of DAEM and gets the better solution by genetic operation. Experiment results on Gaussian mixture model parameter estimation demonstrate that the proposed algorithm can achieve better performance.

Wan, HongJie; Deng, HaoJiang; Wang, XueWei

2013-03-01

209

Optimal Power Train Design of a Hybrid Refuse Collector Vehicle

Optimal Power Train Design of a Hybrid Refuse Collector Vehicle Tobias Knoke, Joachim BÃ¶cker 5251 60 2212 Abstract-- Due to the stop-and-go drive cycle of refuse collector vehicles, hybrid power as an optimization problem with the objectives "minimize fuel consumption" and "minimize vehicle weight

Paderborn, UniversitÃ¤t

210

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

211

A Cryptographic Algorithm Based on Hybrid Cubes

\\u000a Cryptographic algorithms are important to ensure the security of data during transmission or storage. Many algorithms have\\u000a been proposed based on various transformation and manipulation of data. One of which is using magic cube. However, the existing\\u000a approaches are based on a transformation of magic cube’s face values. In this paper, we propose a new cryptographic algorithm\\u000a based on combinations

Sapiee Jamel; Tutut Herawan; Mustafa Mat Deris

2010-01-01

212

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

213

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

214

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

215

Design and Optimization of Future Hybrid and Electric Propulsion Systems

Design and Optimization of Future Hybrid and Electric Propulsion Systems: An Advanced Tool systÃ¨mes de propulsion hybride et Ã©lectrique: un outil avancÃ© et intÃ©grÃ© dans une chaÃ®ne complÃ¨te dÃ©diÃ©e Ã and Optimization of Future Hybrid and Electric Propulsion Systems: An Advanced Tool Integrated in a Complete

Paris-Sud XI, UniversitÃ© de

216

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

217

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing

Myungryun Yoo; Mitsuo Gen

2007-01-01

218

A Hybrid Genetic Algorithm for Classification

In this paper we describe a method for hybridiz ing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algo rithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algo rithm to classify new data records based on their weighted

James D. Kelly Jr.; Lawrence Davis

1991-01-01

219

Algorithm For Optimal Control Of Large Structures

NASA Technical Reports Server (NTRS)

Cost of computation appears competitive with other methods. Problem to compute optimal control of forced response of structure with n degrees of freedom identified in terms of smaller number, r, of vibrational modes. Article begins with Hamilton-Jacobi formulation of mechanics and use of quadratic cost functional. Complexity reduced by alternative approach in which quadratic cost functional expressed in terms of control variables only. Leads to iterative solution of second-order time-integral matrix Volterra equation of second kind containing optimal control vector. Cost of algorithm, measured in terms of number of computations required, is of order of, or less than, cost of prior algoritms applied to similar problems.

Salama, Moktar A.; Garba, John A..; Utku, Senol

1989-01-01

220

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

221

Hybrid algorithms for multiple change-point detection in biological sequences.

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences in high resolution. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many complex diseases. In this study we propose two distinct hybrid algorithms that combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure and the cumulative sum control chart (CUSUM) procedure) with the Cross-Entropy method, which is an evolutionary stochastic optimization technique to estimate both the number of change-points and their corresponding locations in aCGH data. The proposed hybrid algorithms are applied to both artificially generated data and real aCGH experimental data to illustrate their usefulness. Our results show that the proposed methodologies are effective in detecting multiple change-points in biological sequences of continuous measurements. PMID:25381101

Priyadarshana, Madawa; Polushina, Tatiana; Sofronov, Georgy

2015-01-01

222

A hybrid learning algorithm for text classification

Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification that requires fewer documents for training. Instead of using words, word relation i.e association rules from these words is used to derive feature set from preclassified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. Experimental results show that the classifier build this way is more accurate than the existing text classification systems.

Kamruzzaman, S M

2010-01-01

223

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

224

Learning Computer Programs with the Bayesian Optimization Algorithm

of the Bayesian Optimization Algorithm (BOA), a probabilistic model building genetic algorithm, to the domain of program tree evolution. The new system, BOA programming (BOAP), improves significantly on previous algorithms, such as the (hierarchical) Bayesian Optimization Algorithm (BOA) [6]. BOA is asymptotically more

Fernandez, Thomas

225

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.

226

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

227

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

228

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

229

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

230

Decision making in a hybrid genetic algorithm

There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order

F. G. Lobo; D. E. Goldberg

1997-01-01

231

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

232

In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-hGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known NP-hard problem. Objectives described in this paper are to minimize total project time and to minimize total tardiness penalty. However, it is difficult to treat the rc-mPSP problems with traditional optimization techniques. The

Kwan Woo Kim; Youngsu Yun; Jungmo Yoon; Mitsuo Gen; Genji Yamazaki

2005-01-01

233

Genetic algorithm optimization for aerospace electromagnetic design and analysis

This paper provides a tutorial overview of a new approach to optimization for aerospace electromagnetics known as the Genetic Algorithm. Genetic Algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and GA is discussed and the details of GA optimization implementation are explored. The tutorial overview

J. Michael Johnson; Yahya Rahmat-Samii

1996-01-01

234

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

235

Hybrid genetic algorithm with adaptive local search scheme

This paper proposes a hybrid genetic algorithm (a-hGA) with adaptive local search scheme. For designing the a-hGA, a local search technique is incorporated in the loop of genetic algorithm (GA), and whether or not the local search technique is used in the GA is automatically determined by the adaptive local search scheme. Two modes of adaptive local search schemes are

YoungSu Yun

2006-01-01

236

A Hybrid Genetic Algorithm for the Capacitated Vehicle Routing Problem

Recently proved successful for variants of the vehicle routing problem (VRP) involving time windows, genetic algorithms have\\u000a not yet shown to compete or challenge current best search techniques in solving the classical capacitated VRP. In this paper,\\u000a a hybrid genetic algorithm to address the capacitated vehicle routing problem is proposed. The basic scheme consists in concurrently\\u000a evolving two populations of

Jean Berger; Mohamed Barkaoui

2003-01-01

237

Dynamic facility layout problem with hybrid genetic algorithm

Over the past few decades, many optimal and heuristic algorithms have been designed and published to solve the facility layout problem (FLP). These algorithms are mostly static and assume that the flow of materials among facilities is fixed. However, in today's real-world scenario, manufacturing facilities must operate in a dynamic and market-driven environment in which production rates and production mixes

Kazi Shah Nawaz Ripon; Kyrre Glette; Mats Hovin; Jim Torresen

2010-01-01

238

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

239

Do artificial immune systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to

Jon Timmis; Camilla Edmonds; Johnny Kelsey

2004-01-01

240

AMO -- Advanced Modeling and Optimization, Volume 11, Number 1, 2009 A hybrid Hooke and Jeeves. The method is a combination of that of Hooke and Jeeves, and the global optimization algorithm direct of Jones, Perttunen, and Stuckman. The method performs modified iterations of Hooke and Jeeves until

Reale, Marco

241

A new hybrid technique for optimization of a multivariable function is proposed. This method is applied to the problem of complex time Green's function of multilayer media. This technique combines Particle Swarm search algorithm with the gradient based quasi-Newton method. Superiority of the method is demonstrated by comparing its results with other optimization techniques.

Mohsen Ghaffari-Miab; Amin Farmahini-Farahani; Reza Faraji-Dana; Caro Lucas

2007-01-01

242

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations V. Alexandrov1 , E. Atanassov2 , I Equations (SLAE). Monte Carlo meth- ods are used for the stochastic approximation, since it is known experimental results are presented. Keywords: Monte Carlo Method, Markov Chain, Matrix Inversion, So- lution

Dimov, Ivan

243

Study and implementation of hybrid scheduling algorithm on JSP

This paper analyzes the requirement of a practical job-shop scheduling, which is different from the classical job-shop scheduling problems (JSP), more focus on strict resource constraints. First is formulated a mathematical model. Then described a new method, combined the time Petri net and the hybrid genetic algorithm, to solution of the practical JSP. The time Petri net was used to

Zang Daxin; Yao Jianhua; Jin Maozhong

2008-01-01

244

A parallel hybrid genetic algorithm for multiple protein sequence alignment

This paper presents a parallel hybrid genetic algorithm (GA) for solving sum-of-pairs multiple protein sequence alignment. The method is based on a multiple population GENITOR-type GA and involves local search heuristics. It is then extended to parallel to exploit the benefit of a multiprocessor system. Benchmarks from the BAliBASE library are used to validate the method

Hung Dinh Nguyen; Ikuo YOSHIHARA; Kunihito YAMAMORI; Moritoshi YASUNAGA

2002-01-01

245

Hybrid genetic algorithm for transmitter location in wireless networks

Site selection for transmitters in wireless networks is a complex, time-consuming process. Most often, transmitters are located in one of two ways: manually, or through the use of simple geometric models. Unfortunately, each of these methods disregards the most important geographic information affecting the performance of transmitters. This paper introduces a hybrid genetic algorithm designed to automate the site selection

R. M. Krzanowski; J. Raper

1999-01-01

246

An improved hybrid genetic algorithm for the generalized assignment problem

We consider the generalized assignment problem in which the objective is to find a minimum cost assignment of a set of jobs to a set of agents subject to resource constraints. The presented new approach is based on a previously published, successful hybrid genetic algorithm and includes as new features two alternative initialization heuristics, a modified selection and replacement scheme

Harald Feltl; Günther R. Raidl

2004-01-01

247

A hybrid genetic algorithm for the container loading problem

This paper presents a hybrid genetic algorithm (GA) for the container loading problem with boxes of different sizes and a single container for loading. Generated stowage plans include several vertical layers each containing several boxes. Within the procedure, stowage plans are represented by complex data structures closely related to the problem. To generate offspring, specific genetic operators are used that

Andreas Bortfeldt; Hermann Gehring

2001-01-01

248

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

249

Parametric blind-deconvolution algorithm to remove image artifacts in hybrid imaging systems.

Hybrid imaging systems employing cubic phase modulation in the pupil-plane enable significantly increased depth of field, but artifacts in the recovered images are a major problem. We present a parametric blind-deconvolution algorithm, based on minimization of the high-frequency content of the restored image that enables recovery of artifact-free images for a wide range of defocus. We show that the algorithm enables robust matching of the image recovery kernel with the optical point-spread function to enable, for the first time, optimally low noise levels in recovered images. PMID:20721189

Demenikov, Mads; Harvey, Andrew R

2010-08-16

250

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; Kang, Liying; Zhao, Xing-Ming

2014-01-01

251

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

252

Dynamic Vehicle Routing Using Hybrid Genetic Algorithms

This paper presents a novel approach to solving the single-vehicle pickup and delivery problem with time windows and capacity constraints. While dynamic programming has been used to find the optimal routing to a given problem, it requires time exponential in the number of tasks. Therefore, it often fails to find the solutions under real-time conditions in an automated factory. This

Wan-rong Jih; Jane Yung-jen Hsu

1999-01-01

253

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.

254

NASA Astrophysics Data System (ADS)

The antireflection structure (ARS) for solar cells is categorized to mainly two different techniques, i.e., the surface texturing and the single or multi-layer antireflection interference coating. In this study, we propose a novel hybrid ARS, which integrates moth eye texturing and multi-layer coat, for application to organic photovoltaics (OPVs). Using optical simulations based on the finite-difference time-domain (FDTD) method, we conduct nearly global optimization of the geometric parameters characterizing the hybrid ARS. The proposed optimization algorithm consists of two steps: in the first step, we optimize the period and height of moth eye array, in the absence of multi-layer coating. In the second step, we optimize the whole structure of hybrid ARS by using the solution obtained by the first step as the starting search point. The methods of the simple grid search and the Hooke and Jeeves pattern search are used for global and local searches, respectively. In addition, we study the effects of deviations in the geometric parameters of hybrid ARS from their optimized values. The design concept of hybrid ARS is highly beneficial for broadband light trapping in OPVs.

Kubota, Shigeru; Kanomata, Kensaku; Suzuki, Takahiko; Hirose, Fumihiko

2014-10-01

255

OPTIMAL SYNTHESIS, DESIGN AND OPERATION OF HYBRID SEPARATION PROCESSES

or low relative volatilities. In a hybrid process where a distillation column unit and a pervaporation of the other. The addition of a pervaporation unit to the conventional distillation process, either before savings can be achieved when the optimal hybrid process is used instead of distillation or pervaporation

Blandford, Ann

256

Lithium-ion battery aging tests show that battery lifetime can be strongly influenced by the operating conditions, particularly by the state of charge and the cycle depth. Therefore a genetic optimization algorithm is applied to optimize the charging behavior of a plug-in hybrid electric vehicle (PHEV) connected to the grid with respect to maximizing energy trading profits in a vehicle-to-grid (V2G)

Benedikt Lunz; Hannes Walz; Dirk Uwe Sauer

2011-01-01

257

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

258

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, UniversitÃ© de

259

Towards a Genetic Algorithm for Function Optimization Sonja Novkovic

Towards a Genetic Algorithm for Function Optimization Sonja Novkovic and Davor Sverko Abstract: This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, such as non-coding segments, elitist selection and multiple crossover. Key words: Genetic algorithm, Royal

260

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

261

Structural Query Optimization in Native XML Databases: A Hybrid Approach

NASA Astrophysics Data System (ADS)

As XML (eXtensible Mark-up Language) is gaining its popularity in data exchange over the Web, querying XML data has become an important issue to be addressed. In native XML databases (NXD), XML documents are usually modeled as trees and XML queries are typically specified in path expression. The primitive structural relationships are Parent-Child (P-C), Ancestor-Descendant (A-D), sibling and ordered query. Thus, a suitable and compact labeling scheme is crucial to identify these relationships and henceforth to process the query efficiently. We propose a novel labeling scheme consisting of < self-level:parent> to support all these relationships efficiently. Besides, we adopt the decomposition-matching-merging approach for structural query processing and propose a hybrid query optimization technique, TwigINLAB to process and optimize the twig query evaluation. Experimental results indicate that TwigINLAB can process all types of XML queries 15% better than the TwigStack algorithm in terms of execution time in most test cases.

Haw, Su-Cheng; Lee, Chien-Sing

262

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q? = 4.9 % and q? = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data. PMID:22767341

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

263

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

264

Algorithms for the Electrical Optimization of Digital MOS Circuits

Algorithms for the Electrical Optimization of Digital MOS Circuits Kye S. Hedlund, Assistant;1 Abstract This work addresses the problem of automating the electrical optimization of digital MOS circuits algorithms for automating the electrical optimization of digital MOS circuits. Improvements to a circuit

North Carolina at Chapel Hill, University of

265

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

266

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

267

Toward an FPGA architecture optimized for public-key algorithms

Cryptographic algorithms are constantly evolving to meet security needs, and modular arithmetic is an integral part of these algorithms, especially in the case of public-key cryptosystems. To achieve optimal system performance while maintaining physical security, it is desirable to implement cryptographic algorithms in hardware. However, many public- key cryptographic algorithms require the implementation of modular arithmetic, specifically modular multiplication, for

Adam J. Elbirt; Christof Paar

1999-01-01

268

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform Marcos L-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant decrease in compu- tational. This research shows the union of UT and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to give circuit de

Paris-Sud XI, UniversitÃ© de

269

Novel hybrid genetic algorithm for progressive multiple sequence alignment.

The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution. PMID:24084242

Afridi, Muhammad Ishaq

2013-01-01

270

Hybrid quantum scattering algorithms for long-range potentials

NASA Astrophysics Data System (ADS)

We investigate hybrid scattering codes which combine the log-derivative integrator (LOGD) of Johnson at short range with one of the three following integrators at moderate to long range: the multichannel WKB algorithm of Johnson, the variable-interval-variable-step (VIVS) algorithm of Parker, Schmalz, and Light, or a new algorithm (AIRY) based on the linear reference potential algorithm of Gordon modified to propagate the log-derivative matrix directly without determination of perturbation corrections. In calculations of S matrices for the collision of two HF molecules, where the off-diagonal coupling decreases only as R-3, the AIRY integrator is found to be 3 times faster than the WKB integrator, at least 5 times faster than the VIVS integrator, and 19 times faster than direct application of the LOGD integrator in the long-range region.

Alexander, Millard H.

1984-11-01

271

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

272

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

273

Hybrid Kalman/H?filter in designing optimal navigation of vehicle in PRT System

NASA Astrophysics Data System (ADS)

PRT( Personal Rapid Transit ) system is a automated operation, so that it is important exactly finding position of vehicle. Many of PRT system has accepted the GPS system for a position, speed, and direction. in this paper, we propose a combination of Kalman Filter and H? Filter known as Hybrid Kalman/ H? Filter for applying to GPS navigation algorithm. For disturbance cancellation, Kalman Filter is optimal but it requires the statistical information about process and measurement noises while H? Filter only minimizes the "worst-case" error and requires that the noises are bounded. The new Hybrid Filter is expected to reduce the worst-case error and exploit the incomplete knowledge about noises to provide a better estimation. The experiment shows us the ability of Hybrid Filter in GPS navigation algorithm.

Kim, Hyunsoo; Nguyen, Hoang Hieu; Nguyen, Phi Long; Kim, Han Sil; Jang, Young Hwan; Ryu, Myungseon; Choi, Changho

2007-12-01

274

An inverse radiation analysis is presented for estimating the wall emissivities for an absorbing, emitting, scattering media in a two-dimensional irregular geometry with diffusely emitting and reflecting opaque boundaries from the measured temperatures. The finite-volume method was employed to solve the radiative transfer equation for 2D irregular geometry. The hybrid genetic algorithm which contains local optimization algorithm was adopted to

Ki Wan Kim; Seung Wook Baek; Man Young Kim; Hong Sun Ryou

2004-01-01

275

A hybrid genetic algorithm is used to find high-order equivalent circuits (ECs) of synchronous machines using standstill frequency response (SSFR) data. The algorithm performs satisfactorily despite the great deal of local minima surrounding the optimal solution of high-order ECs. It gives circuit parameters that simultaneously fit the three independent transfer functions given by the d-axis two-port network of the synchronous

T. Niewierowicz; R. Escarela-Perez; E. Campero-Littlewood

2003-01-01

276

The ground state energy of the Edwards-Anderson Ising spin glass with a hybrid genetic algorithm

Ground states of three-dimensional Edwards-Anderson ±J Ising spin glasses were calculated with a hybrid of genetic algorithm and local optimization. The algorithm was fast and reliable enough to allow extensive calculations for systems of linear size between 3 and 14 and determination of the average ground state energies with small errors. A linear dependence on 1\\/volume approximates the data very

Károly F. Pál

1996-01-01

277

Search (Tabu-BOA) to electric equipments configuration problems in a power plant. Tabu-BOA is a hybrid Optimization Algorithm(1) and Tabu Search(2)(3) (Tabu-BOA) in order to solve electric equipments configuration. For example, Khan et al have proposed a variation of BOA for multi-objective optimization problems(4

Coello, Carlos A. Coello

278

The research on edge detection algorithm based on hybrid intelligence for color image

NASA Astrophysics Data System (ADS)

Based on the approach of minimization of the cost function, this paper brings forward a kind of hybrid intelligent algorithm for image edge detection, which is based on the particle swarm and quanta evolution algorithm. The implementation of the hybrid intelligent algorithm is also discussed to do the edge detection. The simulation results show that the new algorithm has obtained the satisfied detection effect.

Li, Weiping; Li, Chunyu

2013-03-01

279

A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with

A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with Single Split Paths Words: Genetic Algorithm, Steiner Trees, Point to Multipoint Routing, Telecommunications Network to Multipoint Routing Problem with Single Split Paths. Our hybrid algorithm uses a genetic algorithm

Wainwright, Roger L.

280

DAOmap: A Depth-optimal Area Optimization Mapping Algorithm for FPGA Designs

DAOmap: A Depth-optimal Area Optimization Mapping Algorithm for FPGA Designs Deming Chen, Jason quality. We guarantee optimal mapping depth under the unit delay model. Experimental results show that our to the state-of-the-art depth- optimal, area minimization mapping algorithm CutMap [21], DAOmap is 16

Chen, Deming

281

Efficient inference algorithms for hybrid dynamic Bayesian networks (HDBN)

NASA Astrophysics Data System (ADS)

Bayesian networks for the static as well as for the dynamic cases have been the subject of a great deal of theoretical analysis and practical inference approximations in the research community of artificial intelligence, machine learning and pattern recognition. After exploring the quite well known theory of discrete and continuous Bayesian networks, we introduce an almost instant reasoning scheme to the hybrid Bayesian networks. In addition to illustrate the similarities of the dynamic Bayesian networks (DBN) and the Kalman filter, we present a computationally efficient approach for the inference problem of hybrid dynamic Bayesian networks (HDBN). The proposed method is based on the separations of the dynamic and static nodes, and following hypercubic partitions via the Decision tree algorithm (DT). Experiments show that with high statistical confidence the novel algorithm used in the HDBN performs favorably in the tradeoffs of computational complexities and accuracy performance when compared to Junction tree and Gaussian mixture models on the task of classifications.

Chang, KuoChu; Chen, Hongda

2004-08-01

282

Designing A Hybrid Genetic Algorithm for the Linear Ordering Problem

The Linear Ordering Problem(LOP), which is a well-known \\u000a \\u000a \\u000a \\u000a \\u000a \\u000a NP\\u000a\\\\mathcal{N}\\\\mathcal{P}\\u000a\\u000a \\u000a \\u000a \\u000a \\u000a \\u000a -hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure\\u000a of designing a hybrid genetic algorithm, which includes the selection of crossover\\/mutation operators, accelerating the local\\u000a search module and tuning the parameters. Experimental results show that our hybrid genetic algorithm outperforms

Gaofeng Huang; Andrew Lim

2003-01-01

283

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

284

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

285

A hybrid genetic algorithm for production and distribution

This paper develops a hybrid genetic algorithm for production and distribution problems in multi-factory supply chain models. Supply chain problems usually may involve multi-criterion decision-making, for example operating cost, service level, resources utilization, etc. These criteria are numerous and interrelated. To organize them, analytic hierarchy process (AHP) will be utilized. It provides a systematic approach for decision makers to assign

Felix T. S. Chan; S. H. Chung; Subhash Wadhwa

2005-01-01

286

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

287

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

2014-02-28

288

Stand-alone hybrid wind-photovoltaic power generation systems optimal sizing

NASA Astrophysics Data System (ADS)

Wind and photovoltaic energy resources have attracted energy sectors to generate power on a large scale. A drawback, common to these options, is their unpredictable nature and dependence on day time and meteorological conditions. Fortunately, the problems caused by the variable nature of these resources can be partially overcome by integrating the two resources in proper combination, using the strengths of one source to overcome the weakness of the other. The hybrid systems that combine wind and solar generating units with battery backup can attenuate their individual fluctuations and can match with the power requirements of the beneficiaries. In order to efficiently and economically utilize the hybrid energy system, one optimum match design sizing method is necessary. In this way, literature offers a variety of methods for multi-objective optimal designing of hybrid wind/photovoltaic (WG/PV) generating systems, one of the last being genetic algorithms (GA) and particle swarm optimization (PSO). In this paper, mathematical models of hybrid WG/PV components and a short description of the last proposed multi-objective optimization algorithms are given.

Cr?ciunescu, Aurelian; Popescu, Claudia; Popescu, Mihai; Florea, Leonard Marin

2013-10-01

289

Optimization algorithm for compact slab lasers

NASA Astrophysics Data System (ADS)

The pump structure greatly influences the characteristics of a diode side-pumped laser. To achieve high absorption efficiency and a homogeneous pump-beam distribution simultaneously, a systemic algorithm has been established to optimize the pump structure, where multiple reflections occur on the internal wall of the reflector inside the pump chamber. A novel design of an efficient, highly reliable, and good beam quality diode side-pumped solid-state laser is presented. Effort has been done to obtain a highly uniform pumping intensity in the active area, which simultaneously reduces the effects of thermal gradient. In this design a novel lens duct configuration is used. By this way a uniform power distribution and a maximum absorption of pump power is resulted. Numerical analysis also indicates the superiority of the design to other methods such as direct and diffusive pumping techniques.

Cao, Changqing; Zeng, Xiaodong; An, Yuying

2010-11-01

290

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

291

Cleaner production for continuous digester processes based on hybrid Pareto genetic algorithm.

Pulping production process produces a large amount of wastewater and pollutant emitted, which has become one of the main pollution sources in pulp and paper industry. To solve this problem, it is necessary to implement cleaner production by using modeling and optimization technology. This paper studies the modeling and multi-objective genetic algorithms for continuous digester process. First, model is established, in which environmental pollution and saving energy factors are considered. Then hybrid genetic algorithm based on Pareto stratum-nichecount is designed for finding near-Pareto or Pareto optimal solutions in the problem and a new genetic evaluation and selection mechanism is proposed. Finally using the real data from a pulp mill shows the results of computer simulation. Through comparing with the practical curve of digester, this method can reduce the pollutant effectively and increase the profit while keeping the pulp quality unchanged. PMID:12602617

Jin, Fu-Jiang; Wang, Hui; Li, Ping

2003-01-01

292

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

293

Optimal design for hybrid rocket engine for air launch vehicle

A feasibility study and the optimal design was conducted for the application of a hybrid motor with HTPB\\/LOX combination to\\u000a the first stage of an air launch system. The feasibility analysis showed that the hybrid motor could successfully be used\\u000a as a substitute for the solid rocket motor of the first stage of the Pegasus XL if the average specific

Ihnseok Rhee; Changjin Lee; Jae-Woo Lee

2008-01-01

294

Optimization and realization of a rotor dynamic balance measureing algorithm

Based on the research on the least square influential coefficient method of rotor dynamic balance, to deal with some problems like excessive residual vibration and unsatisfied balance effect, genetic algorithm was introduced to optimize and realize the least square influential coefficient method by using the characteristic of global optimization search. Experimental result shows that the balance algorithm based on genetic

Zi-qiang Zhang; Chuan-jiang Li; Li-li Wan

2010-01-01

295

Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water

oz343 Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water Distribution Systems for determining the optimal schedule of chlorine dosing within a water distribution system considering multiple. The model is also capable of handling improved nonlinear chlorine decay algorithms by separating the genetic

Coello, Carlos A. Coello

296

A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization

Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt to combat this. This proposed work is applied in the medical domain to find the minimal reducts and experimentally compared with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

Suguna, N

2010-01-01

297

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

298

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

299

Airfoil And Wing Design Through Hybrid Optimization Strategies

IntroductionSeveral techniques are today available for designthrough numerical optimization;1concerning inparticular the field of aerodynamic design, beyondmethods developed ad hoc and characterizedby inverse design capabilities, the techniquesmore properly related to direct optimization includemature gradient based methods, and morerecent approaches like automatic di#erentiation,control theory based methods and genetic algorithms (GAs). Generally speaking, it is not possibleto...

A. Vicini; D. Quagliarella

1998-01-01

300

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 ...

301

Bacterial Foraging Optimization Algorithm for neural network learning enhancement

Backpropagation algorithm is widely used to solve many real-world problems, using the concept of Multilayer Perceptron. However, main disadvantages of Backpropagation are the convergence rate of it being relatively slow, and it is often trapped in the local minima. To solve this problem, it is found in the literatures, an evolutionary algorithm such as Particle Swarm Optimization algorithm is applied

Ismail Ahmed A. AL-Hadi; Siti Zaiton Mohd Hashim; Siti Mariyam Hj Shamsuddin

2011-01-01

302

Using a hybrid genetic algorithm and fuzzy logic for metabolic modeling

The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE`s have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA`s convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.

Yen, J.; Lee, B.; Liao, J.C. [Texas A& M Univ., College Station, TX (United States)

1996-12-31

303

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

304

An automated algorithm for stability analysis of hybrid dynamical systems

NASA Astrophysics Data System (ADS)

There are many hybrid dynamical systems encountered in nature and in engineering, that have a large number of subsystems and a large number of switching conditions for transitions between subsystems. Bifurcation analysis of such systems poses a problem, because the detection of periodic orbits and the computation of their Floquet multipliers become difficult in such systems. In this paper we propose an algorithm to solve this problem. It is based on the computation of the fundamental solution matrix over a complete period-where the orbit may contain transitions through a large number of subsystems. The fundamental solution matrix is composed of the exponential matrices for evolution through the subsystems (considered linear time invariant in this paper) and the saltation matrices for the transitions through switching conditions. This matrix is then used to compose a Newton-Raphson search algorithm to converge on the periodic orbit. The algorithm-which has no restriction of the complexity of the system-locates the periodic orbit (stable or unstable), and at the same time computes its Floquet multipliers. The program is written in a sufficiently general way, so that it can be applied to any hybrid dynamical system.

Mandal, K.; Chakraborty, C.; Abusorrah, A.; Al-Hindawi, M. M.; Al-Turki, Y.; Banerjee, S.

2013-07-01

305

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

306

Statistical maritime radar duct estimation using hybrid genetic algorithmMarkov

Statistical maritime radar duct estimation using hybrid genetic algorithmÂMarkov chain Monte Carlo estimation using hybrid genetic algorithmÂMarkov chain Monte Carlo method, Radio Sci., 42, RS3014, doi:10 work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate

Buckingham, Michael

307

Optimization of a hybrid solar energy collector system

OPTIMIZATION OF A HYBRID SOLAR ENERGY COLLECTOR SYSTEM A Thesis by ALAN M. SHI NEMAN Submitted to the Graduate College of Texas A&N University in partial fulfillment of the requirement for the degree MASTER OF SCIENCE May 1981 Major Subject... the optimal crop mix for irrigated and dryland acreages of cotton and grain sorghum in years two through ten of the planning horizon. Machinery is depreciated using the double-declining balance method while buildings and livestock are depreciated using...

Shinkman, Alan M.

1981-01-01

308

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

309

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

NASA Astrophysics Data System (ADS)

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

310

Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained\\u000a optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that\\u000a it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained\\u000a optimization problems and applied to

Dervis Karaboga; Bahriye Basturk

2007-01-01

311

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

312

Evolving Quantum Oracles with Hybrid Quantum-inspired Evolutionary Algorithm

Quantum oracles play key roles in the studies of quantum computation and quantum information. But implementing quantum oracles efficiently with universal quantum gates is a hard work. Motivated by genetic programming, this paper proposes a novel approach to evolve quantum oracles with a hybrid quantum-inspired evolutionary algorithm. The approach codes quantum circuits with numerical values and combines the cost and correctness of quantum circuits into the fitness function. To speed up the calculation of matrix multiplication in the evaluation of individuals, a fast algorithm of matrix multiplication with Kronecker product is also presented. The experiments show the validity and the effects of some parameters of the presented approach. And some characteristics of the novel approach are discussed too.

Ding, S; Yang, Q; Ding, Shengchao; Jin, Zhi; Yang, Qing

2006-01-01

313

Evolving Quantum Oracles with Hybrid Quantum-inspired Evolutionary Algorithm

Quantum oracles play key roles in the studies of quantum computation and quantum information. But implementing quantum oracles efficiently with universal quantum gates is a hard work. Motivated by genetic programming, this paper proposes a novel approach to evolve quantum oracles with a hybrid quantum-inspired evolutionary algorithm. The approach codes quantum circuits with numerical values and combines the cost and correctness of quantum circuits into the fitness function. To speed up the calculation of matrix multiplication in the evaluation of individuals, a fast algorithm of matrix multiplication with Kronecker product is also presented. The experiments show the validity and the effects of some parameters of the presented approach. And some characteristics of the novel approach are discussed too.

Shengchao Ding; Zhi Jin; Qing Yang

2006-10-13

314

Krill herd: A new bio-inspired optimization algorithm

NASA Astrophysics Data System (ADS)

In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (i) movement induced by the presence of other individuals (ii) foraging activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.

Gandomi, Amir Hossein; Alavi, Amir Hossein

2012-12-01

315

Techniques for trajectory optimization using a hybrid computer

NASA Technical Reports Server (NTRS)

The use of a hybrid computer in the solution of trajectory optimization problems is described. The solution technique utilizes the indirect method and requires iterative computation of the initial condition vector of the co-state variables. Convergence of the iteration is assisted by feedback switching and contour modification. A simulation of the method in an on-line updating scheme is presented.

Neely, P. L.

1975-01-01

316

Modeling and optimization of hybrid solar thermoelectric systems with thermosyphons

We present the modeling and optimization of a new hybrid solar thermoelectric (HSTE) system which uses a thermosyphon to passively transfer heat to a bottoming cycle for various applications. A parabolic trough mirror concentrates solar energy onto a selective surface coated thermoelectric to produce electrical power. Meanwhile, a thermosyphon adjacent to the back side of the thermoelectric maintains the temperature

Nenad Miljkovic; Evelyn N. Wang

2011-01-01

317

GenMin: An enhanced genetic algorithm for global optimization

NASA Astrophysics Data System (ADS)

A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the objective function. The test example given takes only a few seconds to run.

Tsoulos, Ioannis G.; Lagaris, I. E.

2008-06-01

318

An Artificial Immune Univariate Marginal Distribution Algorithm

NASA Astrophysics Data System (ADS)

Hybridization is an extremely effective way of improving the performance of the Univariate Marginal Distribution Algorithm (UMDA). Owing to its diversity and memory mechanisms, artificial immune algorithm has been widely used to construct hybrid algorithms with other optimization algorithms. This paper proposes a hybrid algorithm which combines the UMDA with the principle of general artificial immune algorithm. Experimental results on deceptive function of order 3 show that the proposed hybrid algorithm can get more building blocks (BBs) than the UMDA.

Zhang, Qingbin; Kang, Shuo; Gao, Junxiang; Wu, Song; Tian, Yanping

319

Solving constrained optimization problems with hybrid particle swarm optimization

NASA Astrophysics Data System (ADS)

Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.

Zahara, Erwie; Hu, Chia-Hsin

2008-11-01

320

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

321

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 optimal of a car using thermal and electrical energy, which can be combined in different ways. More precisely, different links between the motors and batteries of the car result in different configurations or architec

Bonnans, FrÃ©dÃ©ric

322

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

323

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

324

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

325

An improved hybrid encoding cuckoo search algorithm for 0-1 knapsack problems.

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026

Feng, Yanhong; Jia, Ke; He, Yichao

2014-01-01

326

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026

Feng, Yanhong; Jia, Ke; He, Yichao

2014-01-01

327

An algorithm for the systematic disturbance of optimal rotational solutions

NASA Technical Reports Server (NTRS)

An algorithm for introducing a systematic rotational disturbance into an optimal (i.e., single axis) rotational trajectory is described. This disturbance introduces a motion vector orthogonal to the quaternion-defined optimal rotation axis. By altering the magnitude of this vector, the degree of non-optimality can be controlled. The metric properties of the distortion parameter are described, with analogies to two-dimensional translational motion. This algorithm was implemented in a motion-control program on a three-dimensional graphic workstation. It supports a series of human performance studies on the detectability of rotational trajectory optimality by naive observers.

Grunwald, Arthur J.; Kaiser, Mary K.

1989-01-01

328

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.

329

Delay-area trade-off for MPRM circuits based on hybrid discrete particle swarm optimization

NASA Astrophysics Data System (ADS)

Polarity optimization for mixed polarity Reed—Muller (MPRM) circuits is a combinatorial issue. Based on the study on discrete particle swarm optimization (DPSO) and mixed polarity, the corresponding relation between particle and mixed polarity is established, and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly, mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO (HDPSO). Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model. Finally, the proposed algorithm is testified by MCNC Benchmarks. Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.

Zhidi, Jiang; Zhenhai, Wang; Pengjun, Wang

2013-06-01

330

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

331

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

332

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

333

Hybrid near-optimal aeroassisted orbit transfer plane change trajectories

NASA Technical Reports Server (NTRS)

In this paper, a hybrid methodology is used to determine optimal open loop controls for the atmospheric portion of the aeroassisted plane change problem. The method is hybrid in the sense that it combines the features of numerical collocation with the analytically tractable portions of the problem which result when the two-point boundary value problem is cast in the form of a regular perturbation problem. Various levels of approximation are introduced by eliminating particular collocation parameters and their effect upon problem complexity and required number of nodes is discussed. The results include plane changes of 10, 20, and 30 degrees for a given vehicle.

Calise, Anthony J.; Duckeman, Gregory A.

1994-01-01

334

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

335

Parallel projected variable metric algorithms for unconstrained optimization

NASA Technical Reports Server (NTRS)

The parallel variable metric optimization algorithms of Straeter (1973) and van Laarhoven (1985) are reviewed, and the possible drawbacks of the algorithms are noted. By including Davidon (1975) projections in the variable metric updating, researchers can generalize Straeter's algorithm to a family of parallel projected variable metric algorithms which do not suffer the above drawbacks and which retain quadratic termination. Finally researchers consider the numerical performance of one member of the family on several standard example problems and illustrate how the choice of the displacement vectors affects the performance of the algorithm.

Freeman, T. L.

1989-01-01

336

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

337

An investigation of two network flow optimization algorithms

ABSTRACT An Investigation of Two Network Flow Optimization Algorithms. (May 1973) John Anders Steelquist, B. S. , Baylor University Directed by: Dr. Wilbur L. Meier, Jr. This thesis provides an investigation of two network optimization algorithms... and Conjectures 53 56 V. CONCLUSIONS AND RECOMMENDATIONS FOR FURTHER RESEARCH 64 Conclusions Recommendations 64 68 CHAPTER Page REFERENCES APPENDIX 72 A, OPERATOR'S MANUAL B. INPUT/OUTPUT ROUTINE C. SUBROUTINE SUPERKIL D. SUBROUTINE OUT...

Steelquist, John Anders

2012-06-07

338

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

339

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

340

Finite element-wavelet hybrid algorithm for atmospheric tomography.

Reconstruction of the refractive index fluctuations in the atmosphere, or atmospheric tomography, is an underlying problem of many next generation adaptive optics (AO) systems, such as the multiconjugate adaptive optics or multiobject adaptive optics (MOAO). The dimension of the problem for the extremely large telescopes, such as the European Extremely Large Telescope (E-ELT), suggests the use of iterative schemes as an alternative to the matrix-vector multiply (MVM) methods. Recently, an algorithm based on the wavelet representation of the turbulence has been introduced in [Inverse Probl.29, 085003 (2013)] by the authors to solve the atmospheric tomography using the conjugate gradient iteration. The authors also developed an efficient frequency-dependent preconditioner for the wavelet method in a later work. In this paper we study the computational aspects of the wavelet algorithm. We introduce three new techniques, the dual domain discretization strategy, a scale-dependent preconditioner, and a ground layer multiscale method, to derive a method that is globally O(n), parallelizable, and compact with respect to memory. We present the computational cost estimates and compare the theoretical numerical performance of the resulting finite element-wavelet hybrid algorithm with the MVM. The quality of the method is evaluated in terms of an MOAO simulation for the E-ELT on the European Southern Observatory (ESO) end-to-end simulation system OCTOPUS. The method is compared to the ESO version of the Fractal Iterative Method [Proc. SPIE7736, 77360X (2010)] in terms of quality. PMID:24690653

Yudytskiy, Mykhaylo; Helin, Tapio; Ramlau, Ronny

2014-03-01

341

Evaluation of hybrids algorithms for mass detection in digitalized mammograms

NASA Astrophysics Data System (ADS)

The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.

Cordero, José; Garzón Reyes, Johnson

2011-01-01

342

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

343

Optimal architectural design of parallel and hybrid manipulators

NASA Astrophysics Data System (ADS)

A study is presented of the optimial design of a class of six degree of freedom (DOF) closed-chain manipulators consisting of serial branches, each with joints acting in parallel on a common end effector. Dexterity measures based on instantaneous kinematic characteristics of the manipulator are used as the primary objective in isolating optimum designs. The fully parallel Stewart platform is first examined and a two-parameter family of optimal configurations is shown to exist. A unique optimum Stewart platform architecture is isolated from those possessing optimum local dexterity. The resulting optimum manipulator architecture is one in which the dimensions of the base are twice those of the platform and the linear actuator attachment points at the base and the platform meet in alternating pairs. Hybrid manipulators are then examined. A specific hybrid chain structure is selected from possible six-DOF structures for further investigation. A class of serial chain branches suitable for this structure is defined and it is shown that only five unique branch structures belong to the kinematically simple class. A novel approach to manipulator configuration optimization for optimal local dexterity objectives is introduced and applied to find optimal configurations of hybrid manipulators utilizing the previously identified branch structures.

Pittens, Kenneth H.

344

Identifying Optimal Inorganic Nanomateirals for Hybrid Solar Cells

As a newly developed photovoltaic technology, organic-inorganic hybrid solar cells have attracted great interest because of the combined advantages from both components. An ideal inorganic acceptor should have a band gap of about 1.5 eV and energy levels of frontier orbitals matching those of the organic polymer in hybrid solar cells. Hybrid density functional calculations are performed to search for optimal inorganic nanomaterials for hybrid solar sells based on poly(3-hexylthiophene) (P3HT). Our results demonstrate that InSb quantum dots or quantum wires can have a band gap of about 1.5 eV and highest occupied molecular orbital level about 0.4 eV lower than P3HT, indicating that they are good candidates for use in hybrid solar cells. In addition, we predict that chalcopyrite MgSnSb{sub 2} quantum wire could be a low-cost material for realizing high-efficiency hybrid solar cells.

Xiang, H.; Wei, S. H.; Gong, X. G.

2009-01-01

345

In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro- Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, MFCC, MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000 percent for studio environment and 82.33 percent for office environmental conditions have been achieved i...

Islam, Md Rabiul

2009-01-01

346

Hybrid intelligent control scheme for air heating system using fuzzy logic and genetic algorithm

Fuzzy logic provides a means for converting a linguistic control strategy, based on expert knowledge, into an automatic control strategy. Its performance depends on membership function and rule sets. In the traditional Fuzzy Logic Control (FLC) approach, the optimal membership is formed by trial-and-error method. In this paper, Genetic Algorithm (GA) is applied to generate the optimal membership function of FLC. The membership function thus obtained is utilized in the design of the Hybrid Intelligent Control (HIC) scheme. The investigation is carried out for an Air Heat System (AHS), an important component of drying process. The knowledge of the optimum PID controller designed, is used to develop the traditional FLC scheme. The computational difficulties in finding optimal membership function of traditional FLC is alleviated using GA In the design of HIC scheme. The qualitative performance indices are evaluated for the three control strategies, namely, PID, FLC and HIC. The comparison reveals that the HIC scheme designed based on the hybridization of FLC with GA performs better. Moreover, GA is found to be an effective tool for designing the FLC, eliminating the human interface required to generate the membership functions.

Thyagarajan, T.; Shanmugam, J.; Ponnavaikko, M.; Panda, R.C.

2000-01-01

347

Designing Stochastic Optimization Algorithms for Real-world Applications

NASA Astrophysics Data System (ADS)

This article presents a review of recent advances in stochastic optimization algorithms. Novel algorithms achieving highly adaptive and efficient searches, theoretical analyses to deepen our understanding of search behavior, successful implementation on parallel computers, attempts to build benchmark suites for industrial use, and techniques applied to real-world problems are included. A list of resources is provided.

Someya, Hiroshi; Handa, Hisashi; Koakutsu, Seiichi

348

The optimal path algorithm for emergency rescue for drilling accidents

Addressing flaws in the traditional Dijkstra Algorithm, this paper proposes an improved optimal path algorithm applicable to the GIS drilling accident emergency rescue system. To begin, the paper uses the modified comprehensive analytic hierarchy process to analyze various factors of road conditions, considers the element of urgency, then sets up the digraph with weights of the running time of the

Wenjing Ma; Yingzhuo Xu; Hui Xie

2009-01-01

349

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

350

DIRECT algorithm : A new definition of potentially optimal ...

discussed. Keywords: Global optimization; DIRECT algorithm; Two-points based sampling method; ... described by the following steps: the first step in the algorithm is the initialization, it consists by ..... Future work should be done on numerical tests to compare ... Mountain Conference On Iterative Methods, April 2, (2004).

chiter

2005-08-26

351

Serial and Parallel Genetic Algorithms as Function Optimizers

Parallel genetic algorithms are often very differentfrom the "traditional" genetic algorithmproposed by Holland, especially withregards to population structure and selectionmechanisms. In this paper we compare severalparallel genetic algorithms across a widerange of optimization functions in an attemptto determine whether these changes have positiveor negative impact on their problemsolvingcapabilities. The findings indicatethat the parallel structures perform as well asor ...

V. Scott Gordon; L. Darrell Whitley

1993-01-01

352

Terminating Decision Algorithms Optimally Tuomas Sandholm

often solve larger prob- lem instances than complete ones. The drawback is that one does not know provide at different times, and the algorithm's run-time distribution. We present a linear-time algorithm. Let us define the following symbols: SOLt ="Solution found by time t" (so, if a solution is found

Gordon, Geoffrey J.

353

Application of particle swarm optimization algorithm to image texture classification

NASA Astrophysics Data System (ADS)

This paper describes a kind of robust texture feature invariant to rotation and scale changes, which is the texture energy associated with a mask generated by particle swarm optimization algorithms. The detail procedure and algorithm to generate the mask is discussed in the paper. Furthermore, feature extraction experiments on aerial images are done. Experimental results indicate that the robust feature is effective and PSO-based algorithm is a viable approach for the "tuned" mask training problem.

Ye, Zhiwei; Zheng, Zhaobao; Zhang, Jinping; Yu, Xin

2007-12-01

354

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

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

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

356

In search of optimal clusters using genetic algorithms

Genetic Algorithms (GAs) are generally portrayed as search procedures which can optimize functions based on a limited sample of function values. In this paper, GAs have been used in an attempt to optimize a specified objective function related to a clustering problem. Several experiments on synthetic and real life data sets show the utility of the proposed method. K-Means is

C. A. Murthy; Nirmalya Chowdhury

1996-01-01

357

A memetic algorithm for global optimization in chemical process synthesis

Engineering optimization often deals with very large search spaces which are highly constrained by nonlinear equations that restrict the values of the continuous variables. In this contribution the development of a memetic algorithm (MA) for global optimization in the solution of a problem in the chemical process engineering domain is described. The combination of an evolutionary strategy and a local

Maren Urselmann; Guido Sand; Sebastian Engell

2009-01-01

358

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algo- rithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GA's) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prüfer vector,

Chi-chun Lo; Wei-hsin Chang

2000-01-01

359

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

360

Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The genetic algorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.

Tang, Xinggang; Zhang, Weihong; Zhu, Jihong

361

OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM

This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam [Queensland University of Technology, Brisbane, Queensland (Australia); Ivanovich, Grujica [Ergon Energy, Toowoomba, Queensland (Australia)

2010-06-15

362

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

363

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

364

A parallel variable metric optimization algorithm

NASA Technical Reports Server (NTRS)

An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.

Straeter, T. A.

1973-01-01

365

Stochastic Search for Signal Processing Algorithm Optimization

large number of different but mathematically equivalent formulas. When these formulas are implemented be represented by mathematical formulas and a single signal processing algorithm can be represented by many different, but mathematically equivalent, formulas (Auslander et al., 1996). Interestingly, when

366

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.

367

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, UniversitÃ© de

368

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

369

Approximation algorithms for trilinear optimization with nonconvex ...

Apr 2, 2011 ... In this paper, we study trilinear optimization problems with ... first case is related to the biquadratic form studied by Zhang et al ...... [2] A. Einstein, B. Podolsky and N. Rosen, Can quantum-mechanical description of physical.

2011-04-02

370

Stochastic Search for Signal Processing Algorithm Optimization

large number of different but mathematically equivalent formulas. When these formulas are implemented different, but mathematically equivalent, formulas (Auslander et al., 1996). Interestingly, when optimization aims at finding the fastest formula. We present a new approach that successfully solves

371

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

372

Optimization of catalysts using specific, description-based genetic algorithms.

This paper deals with the key optimization task that has to be solved when improving the performance of many chemical processes--optimization of the catalysts used in the reaction via the optimization of its composition and preparation. A novel approach is presented that allows for the preservation of the advantages of genetic algorithms developed specifically for the optimization of catalytic materials but avoids the disadvantageous necessity to reimplement the algorithm when the scope of the optimized materials changes. Its main idea is to automatically generate problem-tailored implementations from requirements concerning the materials with a program generator. For the specification of such requirements, a formal description language, called catalyst description language, has been developed. PMID:18254615

Holena, Martin; Cukic, Tatjana; Rodemerck, Uwe; Linke, David

2008-02-01

373

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications

NASA Technical Reports Server (NTRS)

Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

374

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

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

2014-01-01

375

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

376

A Genetic Algorithm Approach to Multiple-Response Optimization

Many designed experiments require the simultaneous optimization of multiple responses. A common approach is to use a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors. However, as the problem grows even moderately in either the number of factors or the number of responses, conventional optimization algorithms can fail to find the global optimum. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). This paper proposes and develops a multiple-response solution technique using a GA in conjunction with an unconstrained desirability function. The GA requires that several parameters be determined in order for the algorithm to operate effectively. We perform a robust designed experiment in order to tune the genetic algorithm to perform well regardless of the complexity of the multiple-response optimization problem. The performance of the proposed GA method is evaluated and compared with the performance of the method that combines the desirability with the generalized reduced gradient (GRG) optimization. The evaluation shows that only the proposed GA approach consistently and effectively solves multiple-response problems of varying complexity.

Ortiz, Francisco; Simpson, James R.; Pignatiello, Joseph J.; Heredia-Langner, Alejandro

2004-10-01

377

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

378

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.

379

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

380

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

381

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

382

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

383

Optimization of a Predictive Dialing Algorithm

Most of enterprises rely on the outbound call centers which play a very important role in Customer Relationship Management (CRM) and marketing. Outbound Calls centers that employ predictive dialing system obtain greater productivity and higher service levels by dynamically generating outbound traffic. An algorithm is proposed to provide a trade-off between outbound dialing rate and agent occupation in order to

Sonia Fourati; Sami Tabbane

2010-01-01

384

Terminating Decision Algorithms Optimally Tuomas Sandholm

at different times, and the algorithm's run- time distribution. We present a linear-time algo- rithm a probability estimate that a solution exists. Let us define the following symbols: SOLt ="Solution found solve larger problem instances than complete ones. The drawback is that one does not know whether

Gordon, Geoffrey J.

385

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

386

Parallel sorting algorithms for optimizing particle simulations

Real world particle simulation codes have to handle a huge number of particles and their interactions. Thus, parallel implementations are required to get suitable production codes. Parallel sorting is often used to organize the set of particles or to redistribute data for locality and load balancing concerns. In this article, the use and design of parallel sorting algorithms for parallel

Michael Hofmann; G. Runger; P. Gibbon; R. Speck

2010-01-01

387

A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows

This paper presents a two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows and multiple vehicles (PDPTW). The first stage uses a simple simulated annealing algorithm to decrease the number of routes, while the second stage uses Large neighborhood search (LNS) to decrease total travel cost. Experimental results show the effectiveness of the algorithm which has

Russell Bent; Pascal Van Hentenryck

2006-01-01

388

Sorting Permutations by Reversals through a Hybrid Genetic Algorithm based on Breakpoint Elimination

Sorting Permutations by Reversals through a Hybrid Genetic Algorithm based on Breakpoint belongs to P. In this paper, a standard genetic algorithm for solving the problem of sorting by reversals, an improved genetic algorithm is proposed, that in the initial generations applies reversals

Ayala-RincÃ³n, Mauricio

389

A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. PMID:25105166

Peibo, Duan; Changsheng, Zhang; Bin, Zhang

2014-01-01

390

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

391

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

392

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

393

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

394

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

395

Advanced global optimization algorithms for parameterized LMIs

Parameterized linear matrix inequalities (PLMIs) frequently arise in analysis and synthesis problems of robust control theory. However, in contrast to linear matrix inequalities (LMIs) which are convex optimization problems with available efficient polynomial-time interior-point methods, PLMIs are highly nonconvex and thus are very hard to solve. In this paper, we exploit partial convexity properties of PLMIs that are useful for

H. D. Tuan; P. Apkarian; H. Tuy

1999-01-01

396

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

397

Wind Mill Pattern Optimization using Evolutionary Algorithms

31062 Toulouse Cedex 9, France jean-marc.alliot@irit.fr ABSTRACT When designing a wind farm layout, we]: Design Tools and Techniques--Computer-aided software engineering Keywords wind energy, wind farm layout dramatically; Gon- zalez's recent review [2] lists almost 150 bibliographic refer- ences for the optimal wind-turbine

398

PID Parameters Optimization by Using Genetic Algorithm

Time delays are components that make time-lag in systems response. They arise in physical, chemical, biological and economic systems, as well as in the process of measurement and computation. In this work, we implement Genetic Algorithm (GA) in determining PID controller parameters to compensate the delay in First Order Lag plus Time Delay (FOLPD) and compare the results with Iterative Method and Ziegler-Nichols rule results.

Mirzal, Andri; Furukawa, Masashi

2012-01-01

399

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

400

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

401

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

402

Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence

This paper analyzes convergence properties of the Bayesian optimization algorithm (BOA). It settles the BOA into the framework of problem decomposition used frequently in order to model and understand the behavior of simple genetic algorithms. The growth of the population size and the number of generations until convergence with respect to the size of a problem is theoretically analyzed. The theoretical results are supported by a number of experiments.

Pelikan, M.; Goldberg, D.E.; Cantu-Paz, E.

2000-01-19

403

Genetic algorithm for multi-objective experimental optimization.

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations). PMID:17048033

Link, Hannes; Weuster-Botz, Dirk

2006-12-01

404

A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm

NASA Astrophysics Data System (ADS)

Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.

Mohanty, Prases K.; Parhi, Dayal R.

2014-12-01

405

A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm

NASA Astrophysics Data System (ADS)

Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.

Mohanty, Prases K.; Parhi, Dayal R.

2014-08-01

406

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

407

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

408

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

409

Developing learning algorithms via optimized discretization of continuous dynamical systems.

Most of the existing numerical optimization methods are based upon a discretization of some ordinary differential equations. In order to solve some convex and smooth optimization problems coming from machine learning, in this paper, we develop efficient batch and online algorithms based on a new principle, i.e., the optimized discretization of continuous dynamical systems (ODCDSs). First, a batch learning projected gradient dynamical system with Lyapunov's stability and monotonic property is introduced, and its dynamical behavior guarantees the accuracy of discretization-based optimizer and applicability of line search strategy. Furthermore, under fair assumptions, a new online learning algorithm achieving regret O(?T) or O(logT) is obtained. By using the line search strategy, the proposed batch learning ODCDS exhibits insensitivity to the step sizes and faster decrease. With only a small number of line search steps, the proposed stochastic algorithm shows sufficient stability and approximate optimality. Experimental results demonstrate the correctness of our theoretical analysis and efficiency of our algorithms. PMID:21880573

Tao, Qing; Sun, Zhengya; Kong, Kang

2012-02-01

410

Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization

Multi-join query optimization (MJQO) is an important technique for designing and implementing database manage system. It is\\u000a a crucial factor that affects the capability of database. This paper proposes a new algorithm to solve the problem of MJQO\\u000a based on ant colony optimization (ACO). In this paper, details of the algorithm used to solve MJQO problem have been interpreted,\\u000a including

Nana Li; Yujuan Liu; Yongfeng Dong; Junhua Gu

2008-01-01

411

Codebook design by a hybridization of ant colony with improved LBG algorithm

Ant colony algorithm is a newly emerged stochastic searching optimization algorithm in recent years. In this paper, an appropriately adapted ant colony system embedded with a simple improved LBG algorithm is proposed for vector quantization codebook design. The emphasis is put on the design of the probability transfer function and the tabu list in the ant colony algorithm, the utilization

Li Xia; Luo Xuehui; Zhang Jihong

2003-01-01

412

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

413

Series hybrid vehicles and optimized hydrogen engine design

NASA Astrophysics Data System (ADS)

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(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(x) concentrations must be less than 180 ppm to meet the 0.2 g/mile California Air Resources Board ULEV or Federal Tier-2 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.; Vanblarigan, P.

1995-05-01

414

Optimal Parallel Merging Algorithms on BSR

Merging is one of the most fundamental problems in computer science. It is well known that ?(N\\/p+loglogN) time is required to merge two sorted sequences each of length N on CRCW PRAM with p processors, where p⩽N log?N for any constant ?. We describe two optimal O(1) time solutions to the problem for p=N on BSR (Broadcasting with Selective Reduction).

Limin Xiang; Kazuo Ushijima

2000-01-01

415

Control optimization, stabilization and computer algorithms for aircraft applications

NASA Technical Reports Server (NTRS)

Research related to reliable aircraft design is summarized. Topics discussed include systems reliability optimization, failure detection algorithms, analysis of nonlinear filters, design of compensators incorporating time delays, digital compensator design, estimation for systems with echoes, low-order compensator design, descent-phase controller for 4-D navigation, infinite dimensional mathematical programming problems and optimal control problems with constraints, robust compensator design, numerical methods for the Lyapunov equations, and perturbation methods in linear filtering and control.

1975-01-01

416

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

417

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

418

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

419

Genetic Algorithms Can Improve the Construction of D-Optimal Experimental Designs

algorithms for constructing D- optimal designs are Monte Carlo algorithms, heuristics, that base on the idea that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k better results. Key-Words: - Genetic Algorithm, Memetic Algorithm, Design of Experiments, DOE, D

Zell, Andreas

420

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

421

Multiobjective optimal design of high frequency transformers using genetic algorithm

This paper deals with the multiobjective optimization (MO) design of high frequency (HF) transformers using genetic algorithms (GAs). In its most general form, the design problem requires minimizing the mass or overall dimensions of the core and windings as well as the loss of the transformer while ensuring the satisfaction of a number of constraints. In this contribution, the area

C. Versele; O. Deblecker; J. Lobry

2009-01-01

422

A genetic algorithm for optimizing off-farm irrigation scheduling

This paper examines the use of genetic algorithm (GA) optimization to identify water delivery schedules for an open-channel irrigation system. Significant objectives and important constraints are identified for this system, and suitable representations of these within the GA framework are developed. Objectives include maximizing the number of orders that are scheduled to be delivered at the requested time and minimizing

J. B. Nixon; G. C. Dandy; A. R. Simpson

2001-01-01

423

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

424

A locally optimal handoff algorithm for cellular communications

The design of handoff algorithms for cellular communication systems based on mobile signal strength measurements is considered. The design problem is posed as an optimization to obtain the best tradeoff between the expected number of service failures and expected number of handoffs, where a service failure is defined to be the event that the signal strength falls below a level

Venugopal V. Veeravalli; Owen E. Kelly

1997-01-01

425

HIGHLY PARALLEL EVOLUTIONARY ALGORITHMS FOR GLOBAL OPTIMIZATION, SYMBOLIC INFERENCE AND

, reproduction and selection. Mutation randomly perturbs a candidate solution, recombination decomposes two disÂ tinct solutions and then randomly mixes their parts to form a novel solution, reproduction replicates. G. Degli Antoni. 1 #12; 2. Parallel Cellular Evolutionary Algorithms for Global Optimization

Neumaier, Arnold

426

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

427

Numerical Optimization Algorithms and Software for Systems Biology

The basic aims of this work are: to develop reliable algorithms for solving optimization problems involving large stoi- chiometric matrices; to investigate cyclic dependency between metabolic and macromolecular biosynthetic networks; and to quantify the significance of thermodynamic constraints on prokaryotic metabolism.

Saunders, Michael

2013-02-02

428

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

May 26, 2014 ... value optimization [1], compressed sensing [8, 9, 16], and decomposition methods for large-scale ...... In this section, we prove that Algorithm 1 is globally convergent from remote starting points. ...... Journal of the Institute of Mathematics and Its Applications. 6(1):76–90 ... In: ISBI '09: IEEE International Sym-.

2014-05-26

429

Finding the needle in the haystack: Algorithms for conformational optimization

Algorithms are given for comformational optimization of proteins. The protein folding problems is regarded as a problem of global energy mimimization. Since proteins have hundreds of atoms, finding the lowest-energy conformation in a many-dimensional configuration space becomes a computationally demanding problem.{copyright} {ital American Institute of Physics.}

Andricioaei, I.; Straub, J.E.

1996-09-01

430

Eddy-current testing with the Expected Improvement optimization algorithm

Eddy-current testing with the Expected Improvement optimization algorithm SÂ´andor Bilicz , Emmanuel presents an inverse problem methodology in the domain of non-destructive testing, and more precisely eddy-current in the light of preliminary numerical examples obtained using synthetic data. Keywords: eddy current testing

Paris-Sud XI, UniversitÃ© de

431

Optimization and Benchmark of Cryptographic Algorithms on Network Processors

With the increasing needs of security, cryptographic functions have been exploited in network devices. Besides time consuming, security protocols are flexible in algorithm selections. Fortunately, network processors, which serve as the backbone of intelligent network devices, hold performance and flexibility at the same time. In this article, we investigate several principles that can be used with implementing and optimizing cpptographic

Zhangxi Tan; Chuang Lin; Hao Yin; Bo Li

2004-01-01

432

Optimization and benchmark of cryptographic algorithms on network processors

With the increasing needs of security, cryptographic functions have been exploited in network devices. Besides time consuming, security protocols are flexible in algorithm selections. Fortunately, network processors, which serve as the backbone of intelligent network devices, hold performance and flexibility at the same time. In this article, we investigate several principles that can be used with implementing and optimizing cryptographic

Zhangxi Tan; Chuang Lin; Yanxi Li; Yixin Jiang

2003-01-01

433

An Adaptive Penalty Approach for Constrained GeneticAlgorithm Optimization

). These include: 1. Rejection of infeasible solutions (the death penalty). 2. Using a mapping function so that allAn Adaptive Penalty Approach for Constrained GeneticÂAlgorithm Optimization Khaled Rasheed shehata@cs.rutgers.edu ABSTRACT In this paper we describe a new adaptive penalty approach for handling

Rasheed, Khaled

434

E cient Approximation and Optimization Algorithms for Computational Metrology

E cient Approximation and Optimization Algorithms for Computational Metrology Christian A. Duncan in computational metrology, focusing on the fun- damental issues of \\ atness" and \\roundness." Speci c- ally, we-dimensional point set, which corresponds to the metrology notion of \\ atness," giv- ing an approximation method

Goodrich, Michael T.

435

Optimization of classification tasks by using genetic algorithms

We present an attempt to separate between two kinds of events, using Genetic Algorithms. Events were produced by a Monte Carlo generator and characterized by the most discriminant variables. For the separation between events, two approaches are investigated. First, discriminant function parameters and neural network connection weights are optimized. In a multidimensional search approach, hyper-planes and hyper-surfaces are computed. In

Mostafa Mjahed

2010-01-01

436

Algorithms for Optimizing Production DNA Sequencing Eva Czabarka

of duplex DNA strands, this process, in fact, allows us to sequence one read length from one strand at oneAlgorithms for Optimizing Production DNA Sequencing Eva Czabarka Goran Konjevod Madhav V. Marathe sequenced, reconstructed DNA segment. At first sight, this appears to be computationally hard. We construct

Percus, Allon

437

Comparison between Genetic Algorithms and Particle Swarm Optimization

This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from

Russell C. Eberhart; Yuhui Shi

1998-01-01

438

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

439

Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\\\begin{itemize} more accurate in terms of output precision and model

R. J. Abrahart

2004-01-01

440

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

441

Genetic-algorithm-based path optimization methodology for spatial decision

NASA Astrophysics Data System (ADS)

In this paper, we proposed a method based on GA to solve the path-optimization problem. Unlike the traditional methods, it considers many other factors besides the road length including the task assignment and its balance, which are beyond the capability of path analysis and make this problem a Combinatorial Optimization problem. It can't be solved by a traditional graph-based algorithm. This paper proposes a new algorithm that integrates the Graph Algorithm and Genetic Algorithm together to solve this problem. The traditional Graph-Algorithm is responsible for preprocessing data and GA is responsible for the global optimization. The goal is to find the best combination of paths to meet the requirement of time, cost and the reasonable task assignment. The prototype of this problem is named the TSP (Traveling Salesman Problem) problem and known as NP-Hard Problem. However, we demonstrate how these problems are resolved by the GA without complicated programming, the result proves it's effective. The technique presented in this paper is helpful to those GIS developer working on an intelligent system to provide more effective decision-making.

Yu, Liang; Bian, Fuling

2006-10-01

442

A New Particle Swarm Optimization Algorithm for Dynamic Environments

NASA Astrophysics Data System (ADS)

Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.

Kamosi, Masoud; Hashemi, Ali B.; Meybodi, M. R.

443

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

444

A genetic algorithm approach in interface and surface structure optimization

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.

Zhang, Jian

2010-05-16

445

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.

Oyama, Akira; Liou, Meng-Sing

2001-01-01

446

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

447

Implementation details and experimental results of two communication system prototypes utilizing the hybrid smart-antenna algorithm are described. The first system operates in the 60 GHz frequency band and uses the hybrid smart-antenna algorithm to provide the high antenna gain required to overcome the large propagation losses as well as to eliminate the alignment problem. The second system operating at 2.4

Nuri Celik; Magdy F. Iskander; Zhengqing Yun

448

Fast Optimal Load Balancing Algorithms for 1D Partitioning

One-dimensional decomposition of nonuniform workload arrays for optimal load balancing is investigated. The problem has been studied in the literature as ''chains-on-chains partitioning'' problem. Despite extensive research efforts, heuristics are still used in parallel computing community with the ''hope'' of good decompositions and the ''myth'' of exact algorithms being hard to implement and not runtime efficient. The main objective of this paper is to show that using exact algorithms instead of heuristics yields significant load balance improvements with negligible increase in preprocessing time. We provide detailed pseudocodes of our algorithms so that our results can be easily reproduced. We start with a review of literature on chains-on-chains partitioning problem. We propose improvements on these algorithms as well as efficient implementation tips. We also introduce novel algorithms, which are asymptotically and runtime efficient. We experimented with data sets from two different applications: Sparse matrix computations and Direct volume rendering. Experiments showed that the proposed algorithms are 100 times faster than a single sparse-matrix vector multiplication for 64-way decompositions on average. Experiments also verify that load balance can be significantly improved by using exact algorithms instead of heuristics. These two findings show that exact algorithms with efficient implementations discussed in this paper can effectively replace heuristics.

Pinar, Ali; Aykanat, Cevdet

2002-12-09

449

A scatter learning particle swarm optimization algorithm for multimodal problems.

Particle swarm optimization (PSO) has been proved to be an effective tool for function optimization. Its performance depends heavily on the characteristics of the employed exemplars. This necessitates considering both the fitness and the distribution of exemplars in designing PSO algorithms. Following this idea, we propose a novel PSO variant, called scatter learning PSO algorithm (SLPSOA) for multimodal problems. SLPSOA contains some new algorithmic features while following the basic framework of PSO. It constructs an exemplar pool (EP) that is composed of a certain number of relatively high-quality solutions scattered in the solution space, and requires particles to select their exemplars from EP using the roulette wheel rule. By this means, more promising solution regions can be found. In addition, SLPSOA employs Solis and Wets' algorithm as a local searcher to enhance its fine search ability in the newfound solution regions. To verify the efficiency of the proposed algorithm, we test it on a set of 16 benchmark functions and compare it with six existing typical PSO algorithms. Computational results demonstrate that SLPSOA can prevent premature convergence and produce competitive solutions. PMID:24108491

Ren, Zhigang; Zhang, Aimin; Wen, Changyun; Feng, Zuren

2014-07-01

450

A parallel hybrid genetic algorithm for the vehicle routing problem with time windows

A parallel version of a new hybrid genetic algorithm for the vehicle routing problem with time windows is presented. The route-directed hybrid genetic approach is based upon the simultaneous evolution of two populations of solutions focusing on separate objectives subject to temporal constraint relaxation. While the first population evolves individuals to minimize total traveled distance the second aims at minimizing

Jean Berger; Mohamed Barkaoui

2004-01-01

451

Optimized shear wave generation using hybrid beamforming methods

Elasticity imaging is a medical imaging modality that measures tissue elasticity to aid in diagnosis of certain diseases. Shear wave-based methods have been developed to perform elasticity measurements in soft tissue. These methods often utilize the radiation force mechanism of focused ultrasound to induce shear waves in soft tissue such as liver, kidney, breast, thyroid, and skeletal muscle. The efficiency of the ultrasound beam for producing broadband extended shear waves in soft tissue is very important for widespread use of this modality. Hybrid beamforming combines two types of focusing, conventional spherical and axicon focusing, to produce a beam for generating a shear wave that has increased depth-of–field (DOF) so that measurements can be made with a shear wave with a consistent wave front. Spherical focusing is used in many applications to achieve high lateral resolution, but has low DOF. Axicon focusing, with a cone- shaped transducer can provide good lateral resolution with large DOF. We present our linear aperture design and beam optimization performed using angular spectrum simulations. A large parametric simulation study was performed which included varying the focal depth for the spherical focusing portion of the aperture, the number of elements devoted to spherical and axicon focusing portions of the aperture, and the opening angle used for axicon focusing. The hybrid beamforming method was experimentally tested in two phantoms and the shear wave speed measurement accuracy as well as the DOF for each hybrid beam was evaluated. We compared our results with shear waves generated using only spherical focusing. The results of this study show that hybrid beamforming is capable of producing a beam with increased DOF over which accurate shear wave speed measurements can be made for different size apertures and at different focal depths. PMID:24139918

Nabavizadeh, Alireza; Greenleaf, James F.; Fatemi, Mostafa; Urban, Matthew W.

2013-01-01

452

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.\\u000a An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive\\u000a is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based

Dervis Karaboga; Bahriye Basturk

2007-01-01

453

Optimization and hybridization of membrane-based oxy-combustion power plants

This thesis considers the optimization and hybridization of advanced zero emissions power (AZEP) cycles. More specifically, existing flowsheets for zero and partial emissions are optimized, and new integration schemes with ...

Gunasekaran, Surekha

2013-01-01

454

Multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using Genetic Algorithm

NASA Astrophysics Data System (ADS)

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

Kitagawa, Yosuke; Kitagawa, Koki; Nakamiya, Masaki; Kanazaki, Masahiro; Shimada, Toru

455

NASA Astrophysics Data System (ADS)

Pump-and-treat systems are a common strategy for groundwater remediation, wherein a system of extraction wells is installed at an affected site to address pollutant migration. In this context, the likely performance of candidate remedial systems is often assessed using groundwater flow modeling. When linked with an optimizer, these models can be utilized to identify a least-cost system design that nonetheless satisfies remediation goals. Moreover, the resulting design problems serve as important tools in the development and testing of optimization algorithms. For example, consider EAGLS (Evolutionary Algorithm Guiding Local Search), a recently developed derivative-free simulation-optimization code that seeks to efficiently solve nonlinear problems by hybridizing local and global search techniques. The EAGLS package was designed to specifically target mixed variable problems and has a limited ability to intelligently adapt its behavior to given problem characteristics. For instance, to solve problems in which there are no discrete or integer variables, the EAGLS code defaults to a multi-start asynchronous parallel pattern search. Therefore, to better understand the behavior of EAGLS, the algorithm was applied to a representative dual-plume pump-and-treat containment problem. A series of numerical experiments were performed involving four different formulations of the underlying pump-and-treat optimization problem, namely: (1) optimization of pumping rates, given fixed number of wells at fixed locations; (2) optimization of pumping rates and locations of a fixed number of wells; (3) optimization of pumping rates and number of wells at fixed locations; and (4) optimization of pumping rates, locations, and number of wells. Comparison of the performance of the EAGLS software with alternative search algorithms across different problem formulations yielded new insights for improving the EAGLS algorithm and enhancing its adaptive behavior.

Matott, L. S.; Gray, G. A.

2011-12-01

456

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

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

2005-01-01

457

Modeling and sizing optimization of hybrid photovoltaic/wind power generation system

NASA Astrophysics Data System (ADS)

The rapid industrialization and growth of world's human population have resulted in the unprecedented increase in the demand for energy and in particular electricity. Depletion of fossil fuels and impacts of global warming caused widespread attention using renewable energy sources, especially wind and solar energies. Energy security under varying weather conditions and the corresponding system cost are the two major issues in designing hybrid power generation systems. In this paper, the match evaluation method (MEM) is developed based on renewable energy supply/demand match evaluation criteria to size the proposed system in lowest cost. This work is undertaken with triple objective function: inequality coefficient, correlation coefficient, and annualized cost of system. It provides optimum capacity of as many numbers of supplies as required to match with a load demand in lowest investment, so it can handle large-scale design problems. Meteorological data were collected from the city of Zabol, located in south-east of Iran, as a case study. Six types of wind turbine and also six types of PV modules, with different output powers and costs, are considered for this optimization procedure. A battery storage system is used to even out irregularities in meteorological data. A multi-objective particle swarm optimization algorithm has been used for the prediction of an optimized set of design based on the MEM technique. The results of this study are valuable for evaluating the performance of future stand-alone hybrid power system. It is worth mentioning that the proposed methodology can be effectively employed for any composition of hybrid energy systems in any locations taking into account the meteorological data and the consumer's demand.

Yazdanpanah, Mohammad-Ali

2014-03-01

458

Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

NASA Astrophysics Data System (ADS)

One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

Aleman, Dionne M.; Glaser, Daniel; Romeijn, H. Edwin; Dempsey, James F.

2010-09-01

459

NASA Astrophysics Data System (ADS)

This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

2012-12-01

460

One of the main problems in nucleic acid-based techniques for detection of infectious agents, such as influenza viruses, is that of nucleic acid sequence variation. DNA probes, 70-nt long, some including the nucleotide analog deoxyribose-Inosine (dInosine), were analyzed for hybridization tolerance to different amounts and distributions of mismatching bases, e.g. synonymous mutations, in target DNA. Microsphere-linked 70-mer probes were hybridized in 3M TMAC buffer to biotinylated single-stranded (ss) DNA for subsequent analysis in a Luminex® system. When mismatches interrupted contiguous matching stretches of 6 nt or longer, it had a strong impact on hybridization. Contiguous matching stretches are more important than the same number of matching nucleotides separated by mismatches into several regions. dInosine, but not 5-nitroindole, substitutions at mismatching positions stabilized hybridization remarkably well, comparable to N (4-fold) wobbles in the same positions. In contrast to shorter probes, 70-nt probes with judiciously placed dInosine substitutions and/or wobble positions were remarkably mismatch tolerant, with preserved specificity. An algorithm, NucZip, was constructed to model the nucleation and zipping phases of hybridization, integrating both local and distant binding contributions. It predicted hybridization more exactly than previous algorithms, and has the potential to guide the design of variation-tolerant yet specific probes. PMID:20864443

Ohrmalm, Christina; Jobs, Magnus; Eriksson, Ronnie; Golbob, Sultan; Elfaitouri, Amal; Benachenhou, Farid; Strømme, Maria; Blomberg, Jonas

2010-11-01

461

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

462

Hierarchical artificial bee colony algorithm for RFID network planning optimization.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

463

Using Heuristic Algorithms to Optimize Observing Target Sequences

NASA Astrophysics Data System (ADS)

The preparation of observations is normally carried out at the telescope by the visiting observer. In order to help the observer, we propose several algorithms to automatically optimize the sequence of targets. The optimization consists of assuring that all the chosen targets are observable within the given time interval, and to find their best execution order in terms of the observation quality and the shortest telescope displacement time. Since an exhaustive search is too expensive in time, we researched heuristic algorithms, specifically: Min-Conflict, Non-Sorting Genetic Algorithms and Simulated Annealing. Multiple metaheuristics are used in parallel to swiftly give an approximation of the best solution, with all the constraints satisfied and the total execution time minimized. The optimization process has a duration on the order of tens of seconds, allowing for quick re-adaptation in case of changing atmospheric conditions. The graphical user interface allows the user to control the parameters of the optimization process. Therefore, the search can be adjusted in real time. The module was coded in a way to allow easily the addition of new constraints, and thus ensure its compatibility with different instruments. For now, the application runs as a plug-in to the observation preparation tool called New Short Term Scheduler, which is used on three spectrographs dedicated to the exoplanets search: HARPS at the La Silla observatory, HARPS North at the La Palma observatory and SOPHIE at the Observatoire de Haute-Provence.

Sosnowska, D.; Ouadahi, A.; Buchschacher, N.; Weber, L.; Pepe, F.

2014-05-01

464

Design optimization of a damped hybrid vibration absorber

NASA Astrophysics Data System (ADS)

In this article, the H? optimization design of a hybrid vibration absorber (HVA), including both passive and active elements, for the minimization of the resonant vibration amplitude of a single degree-of-freedom (sdof) vibrating structure is derived by using the fixed-points theory. The optimum tuning parameters are the feedback gain, the tuning frequency, damping and mass ratios of the absorber. The effects of these parameters on the vibration reduction of the primary structure are revealed based on the analytical model. Design parameters of both passive and active elements of the HVA are optimized for the minimization of the resonant vibration amplitude of the primary system. One of the inherent limitations of the traditional passive vibration absorber is that its vibration absorption is low if the mass ratio between the absorber mass and the mass of the primary structure is low. The proposed HVA overcomes this limitation and provides very good vibration reduction performance even at a low mass ratio. The proposed optimized HVA is compared to a recently published HVA designed for similar propose and it shows that the present design requires less energy for the active element of the HVA than the compared design.

Cheung, Y. L.; Wong, W. O.; Cheng, L.

2012-02-01

465

Computational experiments for local search algorithms for binary and mixed integer optimization

In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also ...

Zhou, Jingting, S.M. Massachusetts Institute of Technology

2010-01-01

466

Camera Calibration by Hybrid Hopfield Network and Self- Adaptive Genetic Algorithm

NASA Astrophysics Data System (ADS)

A new approach based on hybrid Hopfield neural network and self-adaptive genetic algorithm for camera calibration is proposed. First, a Hopfield network based on dynamics is structured according to the normal equation obtained from experiment data. The network has 11 neurons, its weights are elements of the symmetrical matrix of the normal equation and keep invariable, whose input vector is corresponding to the right term of normal equation, and its output signals are corresponding to the fitting coefficients of the camera’s projection matrix. At the same time an innovative genetic algorithm is presented to get the global optimization solution, where the cross-over probability and mutation probability are tuned self-adaptively according to the evolution speed factor in longitudinal direction and the aggregation degree factor in lateral direction, respectively. When the system comes to global equilibrium state, the camera’s projection matrix is estimated from the output vector of the Hopfield network, so the camera calibration is completed. Finally, the precision analysis is carried out, which demonstrates that, as opposed to the existing methods, such as Faugeras’s, the proposed approach has high precision, and provides a new scheme for machine vision system and precision manufacture.

Xiang, Wen-Jiang; Zhou, Zhi-Xiong; Ge, Dong-Yuan; Zhang, Qing-Ying; Yao, Qing-He

2012-12-01

467

Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

NASA Astrophysics Data System (ADS)

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2014-07-01

468

Sunspots and Coronal Bright Points Tracking using a Hybrid Algorithm of PSO and Active Contour Model

NASA Astrophysics Data System (ADS)

In the last decades there has been a steady increase of high-resolution data, from ground-based and space-borne solar instruments, and also of solar data volume. These huge image archives require efficient automatic image processing software tools capable of detecting and tracking various features in the solar atmosphere. Results of application of such tools are essential for studies of solar activity evolution, climate change understanding and space weather prediction. The follow up of interplanetary and near-Earth phenomena requires, among others, automatic tracking algorithms that can determine where a feature is located, on successive images taken along the period of observation. Full-disc solar images, obtained both with the ground-based solar telescopes and the instruments onboard the satellites, provide essential observational material for solar physicists and space weather researchers for better understanding the Sun, studying the evolution of various features in the solar atmosphere, and also investigating solar differential rotation by tracking such features along time. Here we demonstrate and discuss the suitability of applying a hybrid Particle Swarm Optimization (PSO) algorithm and Active Contour model for tracking and determining the differential rotation of sunspots and coronal bright points (CBPs) on a set of selected solar images. The results obtained confirm that the proposed approach constitutes a promising tool for investigating the evolution of solar activity and also for automating tracking features on massive solar image archives.

Dorotovic, I.; Shahamatnia, E.; Lorenc, M.; Rybansky, M.; Ribeiro, R. A.; Fonseca, J. M.

2014-02-01

469

Nonconvex Compressed Sensing by Nature-Inspired Optimization Algorithms.

The l0 regularized problem in compressed sensing reconstruction is nonconvex with NP-hard computational complexity. Methods available for such problems fall into one of two types: greedy pursuit methods and thresholding methods, which are characterized by suboptimal fast search strategies. Nature-inspired algorithms for combinatorial optimization are famous for their efficient global search strategies and superior performance for nonconvex and nonlinear problems. In this paper, we study and propose nonconvex compressed sensing for natural images by nature-inspired optimization algorithms. We get measurements by the block-based compressed sampling and introduce an overcomplete dictionary of Ridgelet for image blocks. An atom of this dictionary is identified by the parameters of direction, scale and shift. Of them, direction parameter is important for adapting to directional regularity. So we propose a two-stage reconstruction scheme (TS_RS) of nature-inspired optimization algorithms. In the first reconstruction stage, we design a genetic algorithm for a class of image blocks to acquire the estimation of atomic combinations in all directions; and in the second reconstruction stage, we adopt clonal selection algorithm to search better atomic combinations in the sub-dictionary resulted by the first stage for each image block further on scale and shift parameters. In TS_RS, to reduce the uncertainty and instability of the reconstruction problems, we adopt novel and flexible heuristic searching strategies, which include delicately designing the initialization, operators, evaluating methods, and so on. The experimental results show the efficiency and stability of the proposed TS_RS of nature-inspired algorithms, which outperforms classic greedy and thresholding methods. PMID:25148677

Liu, Fang; Lin, Leping; Jiao, Licheng; Li, Lingling; Yang, Shuyuan; Hou, Biao; Ma, Hongmei; Yang, Li; Xu, Jinghuan

2014-08-19

470

Preliminary flight evaluation of an engine performance optimization algorithm

NASA Technical Reports Server (NTRS)

A performance-seeking control (PSC) algorithm has undergone initial flight test evaluation in subsonic operation of a PW 1128-engined F-15; this algorithm is designed to optimize the quasi-steady performance of an engine for three primary modes: (1) minimum fuel consumption, (2) minimum fan-turbine inlet temperature (FTIT), and (3) maximum thrust. The flight test results have verified a thrust-specific fuel consumption reduction of 1 percent, up to 100 R decreases in FTIT, and increases of as much as 12 percent in maximum thrust. PSC technology promises to be of value in next-generation tactical and transport aircraft.

Lambert, H. H.; Gilyard, G. B.; Chisholm, J. D.; Kerr, L. J.

1991-01-01

471

Preliminary flight evaluation of an engine performance optimization algorithm

NASA Technical Reports Server (NTRS)

A performance seeking control (PSC) algorithm has undergone initial flight test evaluation in subsonic operation of a PW 1128 engined F-15. This algorithm is designed to optimize the quasi-steady performance of an engine for three primary modes: (1) minimum fuel consumption; (2) minimum fan turbine inlet temperature (FTIT); and (3) maximum thrust. The flight test results have verified a thrust specific fuel consumption reduction of 1 pct., up to 100 R decreases in FTIT, and increases of as much as 12 pct. in maximum thrust. PSC technology promises to be of value in next generation tactical and transport aircraft.

Lambert, H. H.; Gilyard, G. B.; Chisholm, J. D.; Kerr, L. J.

1991-01-01

472

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against some realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (FBC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances or uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrade.

Zhao, Y.; Edwards, R.M.; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States)

1997-03-01

473

A hybrid genetic algorithm for the job shop scheduling problems

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. We design a scheduling method based on Single Genetic Algorithm (SGA) and Parallel Genetic Algorithm (PGA). In the scheduling method, the representation,

Byung Joo Park; Hyung Rim Choi; Hyun Soo Kim

2003-01-01

474

A hybrid-optimization method for assessing the realizability of wireframe sketches

NASA Astrophysics Data System (ADS)

This paper introduces an optimization strategy for evaluating the realizability of a 2D wireframe sketch that conveys geometric and topological information about a 3D solid model. Applying the cross-section realizability criterion, one is able to assert whether a wireframe sketch is a true orthogonal projection of a 3D-solid. In this work, we first review current sketch interpretation methods and realizability criteria, and then we focus on an algebraic system derived from the cross-section realizability criterion. A two-phase hybrid-optimization approach for deriving cross-sections of a given wireframe sketch is introduced. In the first phase, a Genetic Algorithm is employed to produce an initial solution (i.e., an initial cross-section), which is refined by a Conjugate Gradient method in the second phase of the proposed approach. The final cross-section is an accurate solution of the aforementioned algebraic system. Then we are able to test sketch's realizability utilizing four criteria which are derived from the cross-section realizability criterion and the applied optimization procedure. The proposed optimization strategy is tested on wireframe sketches with accurate geometry and also on wireframe sketches with inaccurate geometry. Experimental numerical results are presented to illustrate the effectiveness and robustness of the proposed method. [Figure not available: see fulltext.

Azariadis, Philip; Kyratzi, Sofia; Sapidis, Nickolas S.

2013-03-01

475

Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

NASA Astrophysics Data System (ADS)

The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

2014-11-01