【摘要】 针对当前对求解多目标优化的遗传算法中主要考虑如何处理相互冲突的多个目标间的优化,而很少考虑对约束条件的处理的问题,提出一种求解带约束的多目标优化遗传算法,利用邻域比较与存档操作遗传算法处理多个相互冲突的目标之间的优化、利用不可行度选择操作处理约束条件和选用约束主导原理指导进化过程选择操作;面向多目标约束优化算法,列举了2个难点典型问题进行仿真计算研究,仿真结果表明该算法能较大概率地获得多目标约束优化问题的可行Pareto最优解。更多还原

【Abstract】 Genetic algorithms for constrained multi-objective optimization problems mainly focus on optimizing the conflicting multiple objectives without considering the constraint conditions. This paper describes a genetic algorithm which uses neighborhood comparisons and archiving in the genetic algorithm to smooth the conflicting objectives. Infeasibility degree selection is used to handle the constraints with the constraint domain principle applied to guide the evolutionary process. Two classic difficult problems constrained multi-objective optimization were analyzed by the algorithm to show that the method can find feasible Pareto solutions with a large probability.更多还原