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multi-modal function optimization with the ZEDS (zoomed evolutionary dual strategy) algorithmzoomed evolutionary algorithm

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2004年第6期81卷 675-684页

作者： Zhuo, K Yan, L De Garis, H Evans, D Li-Shan, K Wuhan Univ Computat Ctr Wuhan 430072 Peoples R China Utah State Univ Dept Comp Sci Brain Builder Grp Logan UT 84322 USA Nottingham Trent Univ Fac Engn & Comp Nottingham NG1 4BU England

This article introduces a new evolutionary algorithm for multi-modal function optimization called ZEDS (zoomed evolutionary dual strategy). ZEDS employs a two-step, zoomed (global to local), evolutionary approach. In the first (global) step, an improved 'GT algorithm' is employed to perform a global recombinatory search that divides the search space into niches according to the positions of its approximate solutions. In the second (local) step, a 'niche evolutionary strategy' performs a local search in the niches obtained from the first step, which is repeated until acceptable solutions are found. The ZEDS algorithm was applied to some challenging problems with good results, as shown in this article.

关键词： multi-modal function optimization dual strategy evolutionary algorithm global to local search

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multi-modal function optimization based on artificial immune network and chaos

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26th Chinese Control Conference

作者： Deng Jiuying Mao Zongyuan Guangdong Inst Educ Dept Comp Sci Guangzhou 510303 Guangdong Peoples R China South China Univ TechnologyM Coll Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9787811240559

After the immune network algorithms of multi-modal function optimization have developed, their performance can be improved by stochastic chaos map. in chaos attractor equations the variables are steadily approached stable points. A novel algorithm of immune network combined chaos is presented. The solutions searched and optimized can be accelerated using this method. According to opt-aiNet improved, parameters sensitivity can be bated. At last, some functions are tested. Through multi-peak illustrated and results optimized, the approach is verified with high generalized, efficiency and precision.

关键词： multi-modal function optimization artificial immune network chaos variables mixed optimization

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Parallel Quantum Evolutionary Algorithm based on Chaotic Searching Technique for multi-modal function optimization

Parallel Quantum Evolutionary Algorithm based on Chaotic Sea...

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： You, Xiao-ming Miao, Xing-wai Liu, Sheng Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781424442461

A novel Parallel Quantum Evolutionary Algorithm based on Chaotic searching technique (PCQEA) is proposed. In the algorithm, the use of a chaotic searching technique provides this methodology with superior global search ability;several antibody diversification schemes were incorporated into the algorithm in order to enhance the exploitation and exploration. It can help to obtain the multi-modal optimal solutions rapidly. The technique for improving the performance of PCQEA has been described;simulation experiments result shows that the proposed method surpasses the traditional models in regard to the convergence speed.

关键词： Parallel quantum evolutionary algorithm Adaptive operator multi-modal function optimization Chaotic Searching

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A Dynamic Two-Level Evolutionary Algorithm for Solving multi-modal function optimization Problems

A Dynamic Two-Level Evolutionary Algorithm for Solving Multi...

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智能计算及其应用国际会议

作者： GONG Jiafen 1, ZOU Xiufen 1 , JIN Hui2 (1. School of Mathematics and Statistic, Wuhan University, Wuhan 430072,China) (2. Computer Science, Wuhan University, Wuhan 430072,China)

In this paper, based on the two-level subpopulation Evolutionary Algorithm, we proposed a dynamic two-level Evolutionary Algorithm for multi-modal optimization by introducing the concept of Free Energ

关键词： multi-modal function optimization evolutionary algorithm free energy

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A Novel Adaptive Immune-Based multi-modal function optimization Algorithm

A Novel Adaptive Immune-Based Multi-modal Function Optimizat...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.22

作者： Xu Zhang Guoshun Wang Baoliang Li School of Mechanical Engineering Dalian Jiaotong University Dalian China

The multi-modal function optimization is an important problem with a wide-ranging application. In order to find out all optimal solutions and local optimal solutions as many as possible, an adaptive immune-based optimization algorithm is proposed based on analyzing the characteristics and disadvantages of clonal selection algorithm, and combining memory cells producing, network suppression and valley searching method. Testing typical multi-modal functions show this algorithm not only has the less computational efforts and the better search capability, but also can realize adaptive searching without any transcendental presumptions.

关键词： Immune algorithm multi-modal function optimization Adaptive Valley searching method

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Parallel Quantum Evolutionary Algorithm based on Chaotic Searching Technique for multi-modal function optimization

Parallel Quantum Evolutionary Algorithm based on Chaotic Sea...

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2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： Xiao-ming You,Xing-wai Miao and Sheng Liu College of Electronic and Electrical Engineering,Shanghai University of Engineering Science Shanghai,China

A novel Parallel Quantum Evolutionary Algorithm based on Chaotic searching technique(PCQEA)is *** the algorithm,the use of a chaotic searching technique provides this methodology with superior global search ability;several antibody diversification schemes were incorporated into the algorithm in order to enhance the exploitation and *** can help to obtain the multi-modal optimal solutions *** technique for improving the performance of PCQEA has been described;simulation experiments result shows that the proposed method surpasses the traditional models in regard to the convergence speed.

关键词： Parallel quantum evolutionary algorithm Adaptive operator multi-modal function optimization Chaotic Searching

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Quantum evolutionary algorithm based on immune theory for multi-modal function optimization

Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemic...

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Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemical Universities 2007年第3期20卷 45-49页

作者： You, Xiao-Ming Liu, Sheng Shuai, Dian-Xun College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai 201620 China Department of Computer Science and Technology East China University of Science and Technology Shanghai 200237 China

A novel quantum evolutionary algorithm based immune mechanism (MIQEA) for solving function optimization containing multiple global optima was proposed. By niche methods the original population was divided into subpopulations automatically, and then local search was carried by the immune mechanism in which antibody can be clone selected, immune cell can accomplish cross-mutation, memory cells can be produced and similar antibodies can be suppressed for all subpopulations, each subpopulation can obtain optimal solutions. The algorithm can maintain all optimal solutions. The quantum evolutionary algorithm with intrinsic parallelism is integrated with adaptive immune dynamic model, it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and has been able to get the global optimal and sub-optimal solutions rapidly. The convergence of the MIQEA was proved;its superiority is shown by some simulation experiments.

关键词： Cross-mutation Immune operator multi-modal function optimization Quantum evolutionary algorithm

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Parameters control in GAs for dynamic optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第1期6卷 47-63页

作者： Jebari, Khalid Bouroumi, Abdelaziz Ettouhami, Aziz Mohammed V Agdal Univ UM5A Fac Sci Lab Concept & Syst Microelect & Informat Rabat Morocco Hassan II Mohammedia Casablanca Univ Ben Msik Fac Sci Modeling & Instrumentat Lab UH2MC Casablanca Morocco

The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters. Yet the control of parameters has received much attention in the case of static optimization problems, its investigation in the case of dynamic optimization problems (DOPs) is certainly a promising area of search. Indeed, in the case of DOPs the problem is not just to find the optima but to track the moving optima over time, so the parameters must be adapted to this dynamic environment. The proposed algorithm Parameters Control for Dynamic optimization (PCDO) is based on Genetic Algorithm with Fitness Sharing (GAFS). To solve DOPs by controlling GAs parameters, PCDO uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optimums and to perform GAFS. Second, a modified enthusiasm selection is used to adjust the selection pressure. Third, a clustering multi non uniform Mutation is utilized to locate an unexplored search space. Fourth, a novel technique with multiple crossover is applied to guide the algorithm in promising regions of the search space. Fifth, a self adaptive mutation rate is evolved through generation with a learned parameter, in order to control the diversity of the population. In the concern of maintaining the diversity of the population, a new genetic operator called Fertilization is proposed. PCDO is tested on six problems generated from Generalized Dynamic Benchmark Generator (GDBG). Experimental results demonstrate that PCDO outperforms other GAs on DOPs. Moreover, the ability of PCDO to maintain diversity is demonstrated by a new diversity measure.

关键词： Genetic Algorithms Unsupervised Learning Fuzzy Clustering Dynamic optimization Evolutionary Algorithms Fitness Sharing multi-modal function optimization Generalized dynamic benchmark generator (GDBG)

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Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

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Journal of Central South University of Technology 2008年第6期15卷 845-852页

作者：谭冠政周代明江斌 DIOUBATE Mamady I School of Information Science and Engineering Central South University

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

关键词： immune genetic algorithm multi-modal function optimization evolutionary computation elitist selection elitist crossover

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Optimal design of piezoelectric transformer for high efficiency and high power density

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SENSORS AND ACTUATORS A-PHYSICAL 2005年第2期121卷 520-526页

作者： Seo, JM Joo, HW Jung, HK Korea Elect Technol Inst Artificial Intelligence & Mechatron Res Ctr Bucheon 420733 South Korea Seoul Natl Univ Sch Elect Engn Seoul South Korea

The characteristics of piezoelectric transformer, such as resonance frequency, matching impedance, electro-mechanical coupling coefficient and efficiency, are analyzed using the finite element analysis along with the equivalent circuit. The validity of the proposed numerical methods is conformed experimentally. Through all of the verified results, the optimal design for a high efficiency and high power density is determined using the multi-modal optimization algorithm based on an evolution strategy. (c) 2005 Elsevier B.V. All rights reserved.

关键词： piezoelectric transformer finite element method impedance matching electro-mechanical coupling coefficient multi-modal function optimization

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