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Improving Particle Swarm Optimization Using Co-Optimization of Particles and Acceleration Constants

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 552)

Abstract

Particle swarm optimization exhibits effective performance for solving difficulties in real-world problems. However, the determination of acceleration constants, which influence the performance of PSO significantly and varies between different problems, is hard to tune. This paper presents a co-optimization strategy of particles and acceleration constants, which incorporates the optimization of parameters into the basic framework of optimization and extends the dimension of particles and embeds the acceleration constants at the additional part. Experiment results manifest that the proposed algorithm shows satisfactory performance.

Keywords

Particle swarm optimization Co-optimization Acceleration constants 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 61373054, No. 61472164, No. 81301298, No.61302128, No. 61472163. Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025. Science and technology project of Shandong Province under Grant No. 2015GGX101025.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

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