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

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 multi-objective DE variant with an extraordinary mutation strategy and unfixed parameters. Wise tradeoff between convergence and diversity is achieved via the novel cross-generation mutation operators. In addition, a dynamic mechanism enables the parameters to evolve continuously during the optimization process. Empirical results show that the proposed algorithm is powerful in handling multi-objective problems.