Self-adaptive parameters in differential evolution based on fitness performance with a perturbation strategy

Differential evolution (DE) algorithms have been used widely to solve optimization problems and practical cases and have demonstrated high efficiency, performing favorably using only a few parameters. Compared with other traditional algorithms, DE algorithms perform well when used to solve continuous problems. To obtain an approximate solution using DE, it is critical that appropriate parameter values are selected. However, selecting and dynamically tuning the parameter values during evolution are not easy tasks because the values depend significantly on the problem to be solved. To address these issues, this study presents an enhanced DE algorithm with self-adaptive adjustable parameters and a perturbation strategy based on individual fitness performance. Compared with two existing DE algorithms, the proposed algorithm can solve six benchmark functions and has both high efficiency and stability.