Abstract
Parallel computation is an efficient way to combine the advantages of different computation paradigms to obtain promising solution. In order to analyze the performance of parallel computation techniques to the particle swarm optimization (PSO) algorithm, a parallel particle swarm optimization (PPSO) is proposed in this paper. Since the theorem of “no free lunch” exists, there is not an optimization algorithm that can perfectly tackle all problems. The PPSO provides a paradigm to combine different variants of PSO algorithms by using the Message Passing Interface (MPI) so that the advantages of diverse PSO algorithms can be utilized. The PPSO divides the whole evolution process into several stages. At the interval between two successive stages, each PSO algorithm exchanges the achievement of their evolution and then continues with the next stage of evolution. By merging the global model PSO (GPSO), the local model PSO (LPSO), the bare bone PSO (BPSO), and the comprehensive learning PSO (CLPSO), the PPSO achieves higher solution quality than the serial version of these four PSO algorithms, according to the simulation results on benchmark functions.
This work was supported in part by the National High-Technology Research and Development Program (863 Program) of China No.2013AA01A212, in part by the National Natural Science Fundation of China (NSFC) with No. 61402545, 61332002, and 61300044, and in part by the NSFC for Distinguished Young Scholars with No. 61125205.
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Zhang, GW. et al. (2015). Parallel Particle Swarm Optimization Using Message Passing Interface. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_5
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DOI: https://doi.org/10.1007/978-3-319-13359-1_5
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