A novel Differential Evolution (DE) algorithm for multi-objective optimization

摘要: Convergence speed and parametric sensitivity are two issues that tend to be neglected when extending Differential Evolution (DE) for multi-objective optimization. To fill in this gap, we propose a DE variant with an extraordinary mutation strategy unfixed parameters. Wise tradeoff between convergence diversity is achieved via the novel cross-generation operators. In addition, dynamic mechanism enables parameters evolve continuously during optimization process. Empirical results show proposed algorithm powerful handling problems.