A hybrid genetic algorithm and particle swarm optimization for multimodal functions |
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Affiliation: | 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: | Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates. |
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