摘要
Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable DE(SSDE) algorithm,is proposed.Trial vector generation strategies and crossover probability are respectively self-adapted by two operators in this algorithm.Meanwhile,to enhance the convergence rate,vectors selected randomly with the optimal fitness values are introduced to guide searching direction.Benchmark problems are used to verify this algorithm.Compared with other well-known DE algorithms,experiment results indicate that this algorithm is better than other DE algorithms in terms of convergence rate and quality of optimization.
Differential evolution （ DE ） demonstrates good convergence performance, but it is difficult to choose trial vector generation strategies and associated control parameter values. An improved method, self-adapting scalable DE （SSDE） algorithm, is proposed. Trial vector generation strategies and crossover probability are respectively self-adapted by two operators in this algorithm. Meanwhile, to enhance the convergence rate, vectors selected randomly with the optimal fitness values are introduced to guide searching direction. Benchmark problems are used to verify this algorithm. Compared with other well-known DE algorithms, experiment results indicate that this algorithm is better than other DE algorithms in terms of convergence rate and quality of optimization.
基金
National Natural Science Foundation of China (No. 70971020)
