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multimodal function optimization based on particle swarm optimization

IEEE TRANSACTIONS ON MAGNETICS 2006年第4期42卷 1095-1098页

作者： Seo, JH Im, CH Heo, CG Kim, JK Jung, HK Lee, CG Seoul Natl Univ Sch Elect Engn & Comp Sci Seoul 151744 South Korea Dong Eui Univ Dept Elect Engn Pusan 614714 South Korea

In this paper, a new algorithm for the multimodal function optimization is proposed, based on the particle swarm optimization (PSO). A new method, named the multigrouped particle swarm optimization (MGPSO), keeps basic concepts of the PSO, and, thus, shows a more straightforward convergence compared to conventional hybrid type approaches. Moreover, the MGPSO has a unique advantage in that one can search N superior peaks of a multimodal function when the number of groups is N. The usefulness of the proposed algorithm was verified by the application to various case studies, including a practical electromagnetic optimization problem.

关键词： electromagnetic optimization problems multigrouped particle swarm optimization (MGPSO) multimodal function optimization particle swarm optimization (PSO)

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multimodal function optimization Using Minimal Representation Size Clustering and Its Application to Planning Multipaths

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EVOLUTIONARY COMPUTATION 1997年第1期5卷 81-104页

作者： Hocaoglu, Cem Sanderson, Arthur C. Rensselaer Polytech Inst Elect Agile Mfg Res Inst Elect Comp & Syst Engn Dept Troy NY 12180 USA

A novel genetic algorithm (GA) using minimal representation size cluster (MRSC) analysis is designed and implemented for solving multimodal function optimization problems. The problem of multimodal function optimization is framed within a hypothesize-and-test paradigm using minimal representation size (minimal complexity) for species formation and a GA. A multiple-population GA is developed to identify different species. The number of populations, thus the number of different species, is determined by the minimal representation size criterion. Therefore, the proposed algorithm reveals the unknown structure of the multimodal function when a priori knowledge about the function is unknown. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The proposed scheme results in a highly parallel algorithm for finding multiple local minima. In this paper, a path-planning algorithm is also developed based on the MRSC_GA algorithm. The algorithm utilizes MRSC_GA for planning paths for mobile robots, piano-mover problems, and N-link manipulators. The MRSC_GA is used for generating multipaths to provide alternative solutions to the path-planning problem. The generation of alternative solutions is especially important for planning paths in dynamic environments. A novel iterative multiresolution path representation is used as a basis for the GA coding. The effectiveness of the algorithm is demonstrated on a number of two-dimensional path-planning problems.

关键词： Genetic algorithms multimodal function optimization minimal representation size minimum description length cluster analysis species formation path planning multipaths

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A sequential niching memetic algorithm for continuous multimodal function optimization

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APPLIED MATHEMATICS AND COMPUTATION 2012年第17期218卷 8242-8259页

作者： Vitela, Javier E. Castanos, Octavio Univ Nacl Autonoma Mexico Inst Ciencias Nucl Mexico City 04510 DF Mexico

This work discusses a sequential niching algorithm for multiple optimal determination. The procedure consists of a sequence of genetic algorithms (GA) runs, which incorporate a gradient-based hill-climbing algorithm, and make use of a derating function and of niching and clearing techniques to promote the occupation of different niches in the function to be optimized. Thus, the algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches. An effective search of the solution space is stimulated incorporating in the algorithm stages dedicated to find new promising domains in the variable space and stages that exploits the located promising regions. Unlike other algorithms the efficiency of the sequential niching memetic algorithm (SNMA) proposed in this work is not highly sensitive to the niche radius. Performance measurements with 37 standard test functions of dimensions ranging from 1 to 100 show that the SNMA has very good scalability and outperforms other algorithms in accurately locating multiple optima, both global and local. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Memetic algorithms Genetic algorithms multimodal function optimization Multiple optimal determination Niching Local learning

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FRPSO: Fletcher-Reeves based particle swarm optimization for multimodal function optimization

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SOFT COMPUTING 2014年第11期18卷 2227-2243页

作者： Agrawal, Shikha Silakari, Sanjay Rajiv Gandhi Proudyogiki Vishwavidhyalaya Dept Comp Sci & Engn Univ Inst Technol Bhopal MP India

Particle swarm optimization (PSO) is a population-based optimization tool that is inspired by the collective intelligent behavior of birds seeking food. It can be easily implemented and applied to solve various function optimization problems. However, relatively few researchers have explored the potential of PSO for multimodal problems. Although PSO is a simple, easily implemented, and powerful technique, it has a tendency to get trapped in a local optimum. This premature convergence makes it difficult to find global optimum solutions for multimodal problems. A hybrid Fletcher-Reeves based PSO (FRPSO) method is proposed in this paper. It is based on the idea of increasing exploitation of the local optimum, while maintaining a good exploration capability for finding better solutions. In FRPSO, standard PSO is used to update the particle's current position, which is then further refined by the Fletcher-Reeves conjugate gradient method. This enhances the performance of standard PSO. The results of experiments conducted on seventeen benchmark test functions demonstrate that the proposed method shows superior performance on a set of multimodal functions when compared with standard PSO, a genetic algorithm (GA) and fitness distance ratio PSO (FDRPSO).

关键词： Fletcher-Reeves method Local search multimodal function optimization Particle swarm optimization

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Crowding clustering genetic algorithm for multimodal function optimization

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APPLIED SOFT COMPUTING 2008年第1期8卷 88-95页

作者： Ling, Qing Wu, Gang Yang, Zaiyue Wang, Qiuping Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Univ Sci & Technol China Natl Synchrotron Radiat Lab Hefei 230026 Peoples R China

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches and clustering operation to eliminate genetic drift. Numerical experiments on standard test functions indicate that crowding clustering genetic algorithm is superior to both standard crowding and deterministic crowding in quantity, quality and precision of multi-optimum search. The proposed algorithm is applied to the practical optimal design of varied-line-spacing holographic grating and achieves satisfactory results. (c) 2006 Elsevier B.V. All rights reserved.

关键词： multimodal function optimization crowding clustering genetic algorithm evolutionary computation genetic drift varied line-spacing holographic grating

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A novel adaptive sequential niche technique for multimodal function optimization

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NEUROCOMPUTING 2006年第16-18期69卷 2396-2401页

作者： Zhang, Jun huang, De-S Huang Lok, Tat-Ming Lyu, Michael R. Chinese Acad Sci Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Anhui Univ Hefei 230039 Anhui Peoples R China Chinese Univ Hong Kong Dept Informat Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

This paper proposes a novel adaptive sequential niche particle swarm optimization (ASNPSO) algorithm, which uses multiple subswarms to detect optimal solutions sequentially. In this algorithm, the hill valley function is used to determine how to change the fitness of a particle in a sub-swarm run currently. This algorithm has strong and adaptive searching ability. The experimental results show that the proposed ASNPSO algorithm is very effective and efficient in searching for multiple optimal solutions for benchmark test functions without any prior knowledge. (c) 2006 Elsevier B.V. All rights reserved.

关键词： genetic algorithm niche technique particle swarm optimization penalty function multimodal function optimization

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Predication based immune network for multimodal function optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2010年第4期23卷 495-504页

作者： Xu, Qingzheng Wang, Lei Si, Jing Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Xian Commun Inst Xian 710106 Peoples R China

For the problem of indeterminate direction of local search, lacking of efficient regulation mechanism between local search and global search and regenerating new antibodies randomly in the original optimization version of artificial immune network (opt-aiNet), this paper puts forward a novel predication based immune network (PiNet) to solve multimodal function optimization more efficiently, accurately and reliably. The algorithm mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune predication. The proposed algorithm includes two main features with opt-aiNet. The information of antibodies in continuous generations is utilized to point out the direction of local search and to adjust the balance between local and global search. PiNet also employs memory cells to generate new antibodies with high affinities. Theory analysis and experiments on 10 widely used benchmark problems show that when compared with opt-aiNet method, PiNet algorithm is capable of improving search performance significantly in successful rate, convergence speed, search ability, solution quality and algorithm stability. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Natural computing Artificial immune network Immune predication Predication based immune network multimodal function optimization

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A novel algorithm for multimodal function optimization based on evolution strategy

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IEEE TRANSACTIONS ON MAGNETICS 2004年第2期40卷 1224-1227页

作者： Im, CH Kim, HK Jung, HK Choi, K Seoul Natl Univ Sch Elect Engn Seoul 151744 South Korea Korea Electrotechnol Res Inst Adv Power Apparatus Grp Chang Won 641600 South Korea Kangwon Natl Univ Kwangwon Do 200701 South Korea

In this paper, a novel algorithm for multimodal function optimization is proposed, based on the concept of evolution strategy. A new concept, named restricted evolution, shows more improved characteristics than conventional approaches that have been used for multimodal function optimization. The efficiency and usefulness of the proposed method will be verified by the application to various cases including practical optimization problems.

关键词： evolution strategy (ES) multimodal function optimization restricted evolution

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An improved particle swarm optimization algorithm mimicking territorial dispute between groups for multimodal function optimization problems

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IEEE TRANSACTIONS ON MAGNETICS 2008年第6期44卷 1046-1049页

作者： Seo, Jang-Ho Im, Chang-Hwan Kwak, Sang-Yeop Lee, Cheol-Gyun Jung, Hyun-Kyo Seoul Natl Univ Sch Elect Engn & Comp Sci Seoul 151744 South Korea Yonsei Univ Dept Biomed Engn Kangwon Do 220710 South Korea Dong Eui Univ Dept Elect Engn Pusan South Korea

In the present paper, an improved particle swarm optimization (PSO) algorithm for multimodal function optimization is proposed. The new algorithm, named auto-tuning multigrouped PSO (AT-MGPSO) algorithm mimics natural phenomena in ecosystem such as territorial dispute between different group members and immigration of weak groups, resulting in automatic determination of the size of each group's territory and robust convergence. The usefulness of the proposed algorithm is verified by the application to a specially designed test function and a practical electromagnetic optimization problem.

关键词： electromagnetic optimization problems multi-grouped particle swarm optimization (MGPSO) multimodal function optimization particle swarm optimization (PSO)

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Vaccine-Enhanced Artificial Immune System for multimodal function optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2010年第1期40卷 218-228页

作者： Woldemariam, Kumlachew M. Yen, Gary G. Oklahoma State Univ Sch Elect & Comp Engn Intelligent Syst & Control Lab Stillwater OK 74078 USA

This paper emulates a biological notion in vaccines to promote exploration in the search space for solving multimodal function optimization problems using artificial immune systems (AISs). In this method, we first divide the decision space into equal subspaces. The vaccine is then randomly extracted from each subspace. A few of these vaccines, in the form of weakened antigens, are then injected into the algorithm to enhance the exploration of global and local optima. The goal of this process is to lead the antibodies to unexplored areas. Using this biologically motivated notion, we design the vaccine-enhanced AIS for multimodal function optimization, achieving promising performance.

关键词： Artificial immune system (AIS) multimodal function optimization vaccine

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