Hybrid differential evolution algorithm with chaos and generalized opposition-based learning

Authors:
Jing Wang;Zhijian Wu;Hui Wang
Affiliations:
State Key Lab of Software Engineering, Wuhan University, Wuhan, China and School of Software & Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China
Venue:
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Year:
2010

Citing 4
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Abstract

This paper presents a hybrid differential evolution (DE) algorithm based on chaos and generalized opposition-based learning (GOBL). In this algorithm, GOBL strategy transforms current search space into a new search space with a random probability, which provides more opportunities for the algorithm to find the global optimum. When the GOBL strategy isn't executed, the chaotic operator, like a mutation operator, will be introduced to help the DE to jump out local optima and improve the global convergence rate. Simulation results show that this hybrid DE algorithm can electively enhance the searching efficiency and greatly improve the searching quality.