SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

Abstract The differential evolution (DE) algorithm is an efficient random search based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong ability and good robustness. However, performance DE very sensitive to design different operators setting control parameters. To solve these key problems, this paper proposes improved self-adaptive with a shuffled frog-leaping strategy (SFSADE). innovatively incorporates idea into DE, at same time, it cleverly introduces new classification mutation, also designs adaptive adjustment mechanism In addition, we have carried out large number simulation experiments 25 benchmark functions CEC 2005 two nonparametric statistical tests comprehensively evaluate SFSADE. Finally, results show that SFSADE effective in improving significantly improves overall diversity population process dynamic evolution. Compared other advanced variants, its global speed competitiveness.