Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization
针对差分进化在多目标优化中收敛慢和参数敏感的问题，提出了两种新变异算子和参数自适应机制，通过跨代个体混合使用实现收敛与多样性的平衡，实验表明算法优于多种前沿进化算法。
Convergence performance and parametric sensitivity are two issues that tend to be neglected when extending differential evolution (DE) to multiobjective optimization (MO). To fill this research gap, we develop two novel mutation operators and a new parameter adaptation mechanism. A multiobjective DE variant is obtained through integration of the proposed strategies. The main innovation of this paper is the simultaneous use of individuals across generations from an objective-based perspective. Good convergence-diversity tradeoff and satisfactory exploration-exploitation balance are achieved via the hybrid cross-generation mutation operation. Furthermore, the cross-generation adaptation mechanism enables the individuals to self-adapt their associated parameters not only optimization stage-wise but also objective-space-wise. Empirical results indicate the statistical superiority of the proposed algorithm over several state-of-the-art evolutionary algorithms in handling MO problems.