A Trade-off Pareto Solution Algorithm for Multi-objective Optimization

Wang Jing, Zhou Yongsheng, Yang Hao-xiong, Zhan-Ju Hao
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引用次数: 3

Abstract

Most optimization problems in real life are multi-objective optimization problems. The difficulity of multi-objective programming lies in the fact that the objectives are in conflict with each other and an improvement of one objective may lead to the reduction of other objectives. While achieving the global optimal in all objective at the same time is impossible. we use particle swarm optimization to improve the multi-objective patero solution and get the multi-objective trade-off patero optimal solutions. Numerical experiments show that our algorithms are effective, we can get multi-objective patero solutions set and multi-objective trade-offs patero optimal solution at the same time.
多目标优化的权衡- Pareto解算法
现实生活中的大多数优化问题都是多目标优化问题。多目标规划的难点在于目标之间是相互冲突的,一个目标的改进可能导致其他目标的降低。而同时在所有目标上达到全局最优是不可能的。利用粒子群算法对多目标帕特罗算法进行改进,得到了多目标权衡帕特罗最优解。数值实验表明,该算法是有效的,可以同时得到多目标patero解集和多目标权衡patero最优解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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