Binary particle swarm optimization with multiple evolutionary strategies |
| |
Authors: | ZHAO Jing HAN ChongZhao & WEI Bin Ministry of Education Key Lab For Intelligent Networks Network Security State |
| |
Affiliation: | Key Laboratory for Manufacturing Systems Engineering,Institute of Integrated Automation,School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China;2Institute of System Engineering,School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China |
| |
Abstract: | This paper introduces a novel variation of binary particle swarm optimization(BPSO) algorithm and a further extension to improve its performance.Firstly,mimicking the behaviors of some creatures group,multiple evolutionary strategies BPSO(MBPSO) is introduced which takes different evolutionary strategies for various particles according to their performances.Then,on the basis of MBPSO,a new strategy is discussed to improve the performance of the MBPSO(M2BPSO) which adopts the concept of the mutation operator aiming to overcome the premature convergence and slow convergent speed during the later stages of the optimization.The proposed two algorithms are tested on seven benchmark functions and their results are compared with those obtained by other methods.Experimental results show that our methods outperform the other algorithms. |
| |
Keywords: | binary particle swarm optimizer evolutionary strategies swarm intelligence |
本文献已被 CNKI 等数据库收录! |
|