A Comprehensive Analysis of Differential Evolution in Single-Objective Optimization
We perform a comprehensive analysis of Differential Evolution (DE) optimization strategies, in comparison to Genetic Algorithms (GAs). A number of diversified benchmark problems of interest have been considered for single-objective (SO) optimization. We investigate the algorithms behaviour and performance with respect to their parameters setting. We measure the performance in terms of reliability, accuracy and convergence speed.
Evolutionary Algorithm Differential Evolution Genetic Algorithm Single-Objective optimization
ENRICO ZIO GIORGIO VIADANA
Chair at the European Foundation for New Energy, EDF at Ecole Centrale Paris-Supelec, Paris, France Politecnico di Milano, Energy Department, Milano, 20133, Italy
国际会议
北京
英文
914-921
2011-06-20(万方平台首次上网日期,不代表论文的发表时间)