Affiliations: [a] Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. | [b] Department of Computer Science and Entertainment Technology, Tajen University, Pingtung, Taiwan, R.O.C. | [c] Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan, R.O.C.
Correspondence:
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Corresponding author. Ko-Wei Huang, Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. E-mail: [email protected].
Abstract: Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms. However, the PSO converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. To improve the diversity of the PSO, we here propose a memetic algorithm called particle swarm gravitation optimization (PSGO). After a specific number of iterations, some individuals selected from the PSO and GSA systems are exchanged by the roulette wheel approach. Finally, to increase the diversities of the PSO and GSA, we introduce a diversity enhancement operator, which is inspired by the crossover operator used in differential evolution algorithms. In evaluations of five benchmark functions, the PSGO significantly outperformed the PSO and Cuckoo search and yielded a superior performance to the GSA of most of instances and computation times.
Keywords: Meta-heuristic Algorithm, Particle Swarm Optimization, Gravitation Searching Algorithm, Function Optimization Problem Nearring
