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An investigation on niching multiple species based on population replacement strategies for multimodal functions optimization

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SOFT COMPUTING 2010年第1期14卷 49-69页

作者： Li, Minqiang Lin, Dan Kou, Jisong Tianjin Univ Sch Management Tianjin 300072 Peoples R China

This paper studies the niching mechanism based on population replacement in the process of evolution to solve the multimodal functions optimization (MMFO) problems. In order to niche multiple species for the MMFO tasks, the overlapping population replacement is surely needed because the offspring population most probably does not inherit all of the genetic information contained in its parental population, and the basic procedure for niching genetic algorithms with overlapping population replacement is presented. Then four niching schemes, the nearest neighbors replacement crowding (NNRC), the species conservation technique (SCT), the HFC-I (implicit hierarchical fair competition), and the CPE (clearing procedure with elitist) are investigated. These niching schemes are characterized with regard to different niching strategies and parameterizations, and the corresponding niching procedures are outlined. Finally, experiments are carried out on a suite of test functions to compare different niching strategies regarding niching efficiency and scalability. Experimental results illustrate the intrinsic difference of the four niching schemes. The NNRC and HFC-I have a mechanism of multiple species coevolution via adapting multiple species to different niches, while the SCT and CPE tend to make use of a mandatory mechanism to conserve species just like the grid searching over the solution space based on species distance or clearing radius. All niching methods are able to deal with complex MMFO problems, while the NNRC and HFC-I show a better performance in terms of niching efficiency and scalability, and are more robust regarding the algorithm parameterization.

关键词： Genetic algorithms Population replacement Niching strategy Crowding with nearest neighbors replacement Species conserving technique Hierarchical fair competition Clearing procedure Species conservation multimodal functions optimization

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A Modified Glowworm Swarm optimization for multimodal functions

A Modified Glowworm Swarm Optimization for Multimodal Functi...

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23rd Chinese Control and Decision Conference

作者： Zhang, Yu-Li Ma, Xiao-Ping Gu, Ying Miao, Yan-Zi China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9781424487363

Glowworm swarm optimization (GSO) is a novel algorithm for the simultaneous computation of multiple optima of multimodal functions, which is a swarm intelligence based optimization algorithm, such as ant colony optimization (ACO) and particle swarm optimization (PSO). In the optimization of multimode functions, GSO performs very well in terms of the number of peaks captured. In this paper, we propose a modified glowworm swarm optimization algorithm. Variable step-size movement strategy and the self-exploration behavior of glowworms have been studied according to the phenomena of nature. In this way, the behavior of glowworms accords with the biological natural law even more, and easily find multiple optima of a given multimodal function. Simulation experiments on three standard multimodal functions are carried out, and the results show that this modified optimization strategy has nice convergence ability and precision. And the convergence speed of the algorithm is greatly improved.

关键词： multimodal functions optimization Ant colony optimization Particle swarm optimization Glowworm swarm optimization Variable step length movement Self-exploration behavior

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A Modified Glowworm Swarm optimization for multimodal functions

A Modified Glowworm Swarm Optimization for Multimodal Functi...

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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Yu-Li Zhang Yan-Zi Miao Xiao-Ping Ma Ying Gu School of Information and Electrical Engineering China University of Mining and Technology Xuzhou School of Information and Electrical Engineering China University of Mining and Technology Xuzhou School of Science Dalian Jiaotong University Dalian Liaoning 116028 PRC

关键词： multimodal functions optimization Ant colony optimization Particle swarm optimization Glowworm swarm optimization Variable step length movement Self-exploration behavior

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Dynamics of fitness sharing evolutionary algorithms for coevolution of multiple species

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APPLIED SOFT COMPUTING 2010年第3期10卷 832-848页

作者： Li, Minqiang Lin, Dan Kou, Jisong Tianjin Univ Sch Management Tianjin 300072 Peoples R China

This paper builds the normal model of fitness sharing with proportionate selection on real-valued functions, and derives the dynamic formula to describe the evolution process of the population with the fitness sharing. The normal modeling simulation is investigated on specific test functions, and experimental results illustrate that the normal model is able to describe exactly the dynamics of the fitness sharing EAs and is a good platform to study the behavior of the fitness sharing EAs with regard to niching radius. The experimental results of the normal modeling simulation and the fitness sharing EAs verify the dilemma in finding optimal niche radius to achieve both good niching convergence and niching efficiency, for which a hybrid scheme is proposed to carry out the niching task. (C) 2009 Elsevier B. V. All rights reserved.

关键词： Evolutionary algorithms Fitness sharing Niching Normal modeling multimodal functions optimization

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New Particle Swarm optimization Based on Sub-groups Mutation 14th

New Particle Swarm Optimization Based on Sub-groups Mutation

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14th International Conference on Intelligent Computing (ICIC)

作者： Zhou Fengli Lin Xiaoli Wuhan Univ Sci & Technol City Coll Fac Informat Engn Wuhan 430083 Hubei Peoples R China

ISBN: (纸本)9783319959573;9783319959566

In order to overcome the premature convergence of particle swarm optimization (PSO) algorithm, an improved PSO algorithm based on sub-groups mutation (SsMPSO) is proposed. This algorithm has proposed the sub-groups with random directional vibrating search to mutate the global optimal position of the main swarm and changed the way of random mutation. The mutation based on sub-groups enabled the algorithm had excellent local exploit ability and circumvented the premature convergence. It used another mutation on bad particles to enhance the algorithm's global exploit ability and expand the searching space. Finally, high dimension benchmark functions have been used to test the performance of improved algorithm. The simulation results show that the proposed algorithm can effectively overcome the premature problem, the multimodal function optimization can avoid local extreme point and the convergence and convergence accuracy are greatly improved.

关键词： Premature convergence Random directional vibrating exploit Sub-groups Mutation multimodal functions optimization

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Multi-modal search for multiobjective optimization: an application to optimal smart grids management

Multi-modal search for multiobjective optimization: an appli...

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Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion

作者： Eleonora Riva Sanseverino Maria Luisa Di Silvestre Roberto Gallea DIEETCAM Universita di Palermo DICGIM Universita di Palermo

ISBN: (纸本)9781627483889

This paper studies the possibility to use efficient multi-modal optimizers for multi-objective optimization. In this paper, the application area considered for such new approach is the optimal dispatch of energy sources in smart microgrids. The problem indeed shows a non uniform Pareto front and requires efficient optimal search methods. The idea is to exploit the potential of agents in population-based heuristics to improve diversity in the Pareto front, where solutions show the same rank and are thus equally weighted. Since Pareto dominance is at the basis of the theory of multi-objective optimization, most algorithms show the non dominance ranking as quality indicator, with some problem in finding sufficiently diverse solutions. Other algorithms, such as the Indicator Based Evolutionary Algorithm, use most commonly the Hypervolume indicator which also intrinsically shows diversity preserving problems. In this paper, the Glow-worm swarm optimizer is used as multimodal optimization method over a set of solutions ordered based on non dominance. After the introduction of this algorithm, its multiobjective implementation is briefly outlined. Then some tests are carried out on test functions taken from the literature giving quite encouraging results. Finally, the problem of optimal energy dispatch in smart microgrids is described and different applications are shown comparing the results with those obtained emplying the Non Dominated Sorting Genetic Algorithm II.

关键词： multimodal functions optimization Optimal management Distributed energy resources Multi-objective evolutionary optimization Smart grids

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