A self-adaptive differential evolution algorithm for binary CSPs
Hongjie Fu, Dantong Ouyang, Jiaming Xu · 2010 Sixth International Conference on Natural Computation · 2010
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 of an individual's exploration and exploitation capability for different evolving phase, F and CR equal to two different self-adjusted nonlinear functions. F and CR vary 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.