A backtracking differential evolution with multi-mutation strategies autonomy and collaboration

This paper presents a backtracking differential evolution with multi-mutation strategies autonomy and collaboration (bDE-MsAC) to solve the optimization problems. In the proposed bDE-MsAC, five modified mutation strategies are employed to simultaneously construct a global exploration domain (GED) and a local exploitation domain (LED). Then, a mechanism of multi-mutation strategies autonomy and collaboration is introduced to realize the coevolution between GED and LED. Besides, the parameter adaptation scheme based on individual similarity and evolution status can adaptively update the parameters and bring vitality to the evolution process. Meanwhile, an evolution backtracking strategy is designed to control the population diversity. The population can trace back to the generation with maximum best fitness descent and then change the search direction to avoid the premature. Comparison results with nine DE algorithms on the well-known test functions reveal that the proposed bDE-MsAC has a competitive performance in comparison with other DE methods. In addition, the experiments analyze the effect of two key parameters and demonstrate the effectiveness and superiority of the evolution backtracking strategy.