A memetic particle swarm optimization algorithm for multimodal optimization problems

Authors:
Hongfeng Wang;Ilkyeong Moon;Shenxiang Yang;Dingwei Wang
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China and Department of Industrial Engineering, Pusan National University, Pusan, Republic of Korea and Sta ...;Department of Industrial Engineering, Pusan National University, Pusan, Republic of Korea;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China and Department of Information Systems and Computing, Brunel University, Uxbri ...;School of Information Science and Engineering, Northeastern University, Shenyang 110819, China and State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, S ...
Venue:
Information Sciences: an International Journal
Year:
2012

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Abstract

Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal solutions. In this paper, a memetic algorithm that hybridizes particle swarm optimization (PSO) with a local search (LS) technique, called memetic PSO (MPSO), is proposed for locating multiple global and local optimal solutions in the fitness landscape of MMOPs. Within the framework of the proposed MPSO algorithm, a local PSO model, where the particles adaptively form different species based on their indices in the population to search for different sub-regions in the fitness landscape in parallel, is used for globally rough exploration, and an adaptive LS method, which employs two different LS operators in a cooperative way, is proposed for locally refining exploitation. In addition, a triggered re-initialization scheme, where a species is re-initialized once converged, is introduced into the MPSO algorithm in order to enhance its performance of solving MMOPs. Based on a set of benchmark functions, experiments are carried out to investigate the performance of the MPSO algorithm in comparison with some EAs taken from the literature. The experimental results show the efficiency of the MPSO algorithm for solving MMOPs.