A self-adaptive differential evolution algorithm for binary CSPs

Authors:
Hongjie Fu;Dantong Ouyang;Jiaming Xu
Affiliations:
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China and College of Computer Science and Technology, Jilin Teachers ...;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;China Internet Network Information Center, Beijing 100000, China
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

A novel self-adaptive differential evolution (SADE) algorithm is proposed in this paper. SADE adjusts the mutation rate F and the crossover rate CR adaptively, taking account of the different distribution of population. In order to balance an individual's exploration and exploitation capability for different evolving phases, F and CR are equal to two different self-adjusted nonlinear functions. Attention is concentrated on varying F and CR dynamically with each generation evolution. SADE maintains the diversity of population and improves the global convergence ability. It also improves the efficiency and success rate and avoids the premature convergence. Simulation and comparisons based on test-sets of CSPs demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.