Comprehensive Learning Particle Swarm Optimization with Tabu Operator Based on Ripple Neighborhood for Global Optimization

被引：1

作者：

Qi, Jin ^{[1
]}

Xu, Bin ^{[1
]}

Wang, Kun ^{[1
]}

Yin, Xi ^{[2
]}

Hu, Xiaoxuan ^{[2
]}

Sun, Yanfei ^{[2
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing, Jiangsu, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF THE 11TH EAI INTERNATIONAL CONFERENCE ON HETEROGENEOUS NETWORKING FOR QUALITY, RELIABILITY, SECURITY AND ROBUSTNESS | 2015年

关键词：

comprehensive learning particle swarm optimizer (CLPSO); Tabu search; Gaussian distribution; parameter adaptive;

D O I：

10.4108/eai.19-8-2015.2260857

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

For the weak convergence at the latter stage of the comprehensive learning particle swarm optimizer (CLPSO), we put forward a new CLPSO based on Tabu search to enhance the performance. Inspired by the phenomenon of water waves, a Ripple Neighborhood (RP) structure based on the Gaussian distribution is proposed to construct a new adaptive neighborhood structure to guide the selection of candidate solutions in Tabu search, which solves the problem of low convergence and improves the quality of the solution in CLPSO. Experimental results on the standard 26 test functions show that the proposed algorithm achieves a better performance compared with CLPSO.

引用

页码：280 / 286

页数：7

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