A variant of differential evolution based on permutation regulation mechanism

Authors:
Dazhi Jiang;Hui Wang;Zhijian Wu
Affiliations:
Department of Computer Science, Shantou University, Shantou, China;The State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China;The State Key Laboratory 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 3
Cited 0

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Differential evolution (DE) is a stochastic, population based search method, which has emerged as a powerful tool for solving optimization problems. This paper presents a novel algorithm based on traditional DE and permutation regulation mechanism to enhance the performance of DE. As a kind of enhanced learning strategy, the permutation regulation mechanism, which makes efforts in the evolving, is constructed by rearranging the selected three father vectors. In order to verify the performance of the proposed algorithm, two experiments on some well-known benchmark functions are conducted. Performance compared with other three DE variants confirms that the new algorithm outperforms better in terms of solution accuracy.