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A Novel Multi-population Particle Swarm Optimization with Learning Patterns Evolved by Genetic Algorithm

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 10956)

Abstract

In recent years, particle swarm optimization (PSO) and genetic algorithm (GA) have been applied to solve various real-world problems. However, the original PSO is based on single population whose learning patterns (inertia weights, learning factors) has no potentials in evolution. All particles in the population interact and search according to a fixed pattern, which leads to the reduction of population diversity in the later iterations and premature convergence on complex and multi-modal problems. Therefore, a novel multi-population PSO with learning patterns evolved by GA is proposed to improve diversity and exploration capabilities of populations. Meanwhile, the local search of PSO particles which start in the same position also evolved by GA independently maintains exploitation ability inside each sub population. Experimental results show that the accuracy is comparable and our method improves the convergence speed.

Keywords

  • Co-evolutionary computation
  • Genetic algorithm
  • Particle swarm optimization

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Acknowledgment

This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 81671785, No. 61472164, No. 61472163, No. 61672262. Science and technology project of Shandong Province under Grant No. 2015GGX101025. Project of Shandong Province Higher Educational Science and Technology Program under Grant no. J16LN07. Shandong Provincial Key R&D Program under Grant No. 2016ZDJS01A12, No. 2016GGX101001.

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Correspondence to Qingbei Guo or Lin Wang .

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Liu, C., Sun, F., Guo, Q., Wang, L., Yang, B. (2018). A Novel Multi-population Particle Swarm Optimization with Learning Patterns Evolved by Genetic Algorithm. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_9

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