A novel particle swarm optimizer hybridized with extremal optimization |
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Authors: | Min-Rong Chen Xia Li Xi Zhang Yong-Zai Lu |
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Affiliation: | 1. LRIA (USTHB) and University of Boumerdes, Boumerdes, 35000, Algeria\n;2. Ferhat Abbas Setif University 1, Setif, 19000 and LRIA-USTHB, Algeria;3. LREEI, University of Boumerdes, Boumerdes, 35000, Algeria;1. School of Computer Science, Wuhan University, Wuhan 430072, China;2. College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China;3. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China;4. School of Electrical Engineering, Wuhan University, Wuhan 430072, China;5. School of Economics and Management, Wuhan University, Wuhan 430072, China |
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Abstract: | Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO–EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. We testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other meta-heuristics. The proposed approach is shown to have superior performance and great capability of preventing premature convergence across it comparing favorably with the other algorithms. |
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