A particle swarm optimization based memetic algorithm for dynamic optimization problems |
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| Authors: | Hongfeng Wang Shengxiang Yang W H Ip Dingwei Wang |
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| Affiliation: | (1) School of Information Science and Engineering, Northeastern University, Shenyang, 110004, People’s Republic of China;(2) Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH, UK;(3) Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China;(4) Department of Industrial Engineering, Pusan National University, Pusan, Korea |
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| Abstract: | Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems
since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based
memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework
of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator
and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random
immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks
in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic
algorithm is robust and adaptable in dynamic environments. |
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