增强差异演化算法及其应用

针对约束优化问题，提出一种适于约束优化的增强差异演化算法(enhanced differential evolution algorithm for constrained optimization， ECDE).在约束处理上采用不可行域与可行域更新规则的方法，避免了传统的惩罚函数方法中对惩罚因子的设置，使算法的实现变得简单.改进了DE算法的变异操作，对选择的3个父代个体进行操作遍历，产生6个候选解，取适应值最优的为变异操作的解，大大改善了算法的稳定性、鲁棒性和搜索性能.通过4个测试函数和1个设计实例仿真，表明所提出的算法具有较快的收敛速度和较好的稳定性和鲁棒性.

Enhanced differential evolution and its application

This paper proposed an enhanced differential evolution algorithm for constrained optimization modified differential evolution algorithm(ECDE) to solve constraints optimization problems. The algorithm used three simple selection criteria based on feasibility and infeasibility to guide the search. The proposed algorithm didn't adopt the penalty function method， in contrast to the penalty function method， the constraint-handing technique of this algorithm was very simple， which didn't require additional parameters. In addition， this paper improved the mutation procedure of DE algorithm， operation traversal has been applied to the selection of the three parent individuals，which generated six candidate solutions， and then adopted to the best fitness of the six candidate solutions for the mutation solution. For these measures being adopted， the stability， robustness and global searching performance of DE algorithm were improved greatly. Results of simulations and comparisons with the other algorithms based on four testing functions demonstrate the effectiveness， efficiency and robustness of the proposed ECDE.