Preferred Learning-based Multiple Individuals Differential Evolution Algorithm for Constrained Optimization Problems

doi:10.3969/j.issn.1006-2475.2015.10.004

Abstract

Abstract: This paper presents a preferred learning-based multiple individuals differential evolution algorithm for solving constrained optimization problems, in order to improve the search ability of difference evolution algorithm. Firstly, a kind of preferred learning strategy is applied to the hybrid mutation operator, which makes it quickly search to the area of feasible solution, then the clone strategies are used to enhance the search intensity in optimal solution area and the ability of local search is greatly improved. Finally the algorithm is tested on the CEC2006 classic Benchmark function, the experimental results show that the algorithm is of better results in solving efficiency and accuracy.

Key words: differential evolution algorithm, constrained optimization , clone, preferred learning

CLC Number:

TP391

FU Yu-zhen1, ZHOU Jie-wen1, SHAO long-qiu2, ZHANG Hui1. Preferred Learning-based Multiple Individuals Differential Evolution Algorithm for Constrained Optimization Problems[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.10.004.

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