Particle swarm optimizer with two differential mutation

Chen, Yonggang1,2,3; Li, Lixiang3; Peng, Haipeng3; Xiao, Jinghua4; Yang, Yixian3; Shi, Yuhui5

2017-12

10.1016/j.asoc.2017.07.020

APPLIED SOFT COMPUTING   影响因子和分区

1568-4946

1872-9681

In this article, a particle swarm optimization algorithm with two differential mutation (PSOTD) is proposed. In PSOTD, a novel structure with two swarms and two layers (bottom layer and top layer) is designed. The top layer consists of all the personal best particles, and the bottom layer consists of all the particles. We divide the particles in the top layer into two sub-swarms. Two different differential mutation operations with two different control parameters are employed in order to breed the particles in the top layer. Thus, one sub-swarm has a good exploration capability, and the other sub-swarm has a good exploitation capability. Obviously, since the top layer leads the bottom layer, the bottom particles achieve a good trade-off between exploration and exploitation. Under the searching structure, PSO enhances the global search capability and search efficiency. In order to test the performance of PSOTD, 44 benchmark functions widely adopted in the literature are used. The experimental results demonstrate that the proposed PSOTD outperforms most of the other tested variants of the PSO in terms of both solution quality and efficiency. (C) 2017 Published by Elsevier B.V.

Swarm intelligence Particle swarm optimization Differential evolution Global optimization

SCI ; EI

英语

其他

National Natural Science Foundation of China[61472045] ; National Natural Science Foundation of China[61573067] ; National Natural Science Foundation of China[61771071] ; National Natural Science Foundation of China[61273367]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications

WOS:000417629800023

ELSEVIER SCIENCE BV

20173604107965

Benchmarking ; Economic and social effects ; Efficiency ; Global optimization ; Swarm intelligence

Production Engineering:913.1 ; Optimization Techniques:921.5 ; Social Sciences:971

COMPUTER SCIENCE

Web of Science

1.Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
2.Henan Univ Sci & Technol, Informat Engn Coll, Luoyang 471023, Peoples R China
3.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China
4.Beijing Univ Posts & Telecommun, Sch Sci, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China
5.Southern Univ Sci & Technol, Comp Sci & Engn, Shenzhen, Peoples R China

Chen, Yonggang,Li, Lixiang,Peng, Haipeng,et al. Particle swarm optimizer with two differential mutation[J]. APPLIED SOFT COMPUTING,2017,61:314-330.

Chen, Yonggang,Li, Lixiang,Peng, Haipeng,Xiao, Jinghua,Yang, Yixian,&Shi, Yuhui.(2017).Particle swarm optimizer with two differential mutation.APPLIED SOFT COMPUTING,61,314-330.

Chen, Yonggang,et al."Particle swarm optimizer with two differential mutation".APPLIED SOFT COMPUTING 61(2017):314-330.