A New Evolutionary Decision Theory for Many-Objective Optimization Problems
A New Evolutionary Decision Theory for Many-Objective Optimization Problems
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DOI：
10.1007/978-3-540-74581-5_1
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Zhuo Kang;Lishan Kang;Xiufen Zou;Minzhong Liu;Changhe Li;Ming Yang;Yan Li;Yuping Chen;Sanyou Ze
中科院分区：
文献类型：
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作者：
Zhuo Kang;Lishan Kang;Xiufen Zou;Minzhong Liu;Changhe Li;Ming Yang;Yan Li;Yuping Chen;Sanyou Ze
本文指出，对于多目标优化问题，Pareto最优是不公平的、不合理的和不完善的。本文的主要贡献是发现了一种新的最优性定义--ε-最优性，该定义基于一种新的概念--ε-优势，它不仅考虑了两个可行解之间的上级和下级目标的数目之差，而且考虑了问题中所有目标具有同等重要性的假设下改进的目标函数的值。给出了两种新的进化算法，其中ε-优势作为选择策略，获胜分数作为精英策略，用于搜索最优解。设计了两个基准问题来测试多目标优化问题的新概念。数值实验表明，新的最优性定义比进化计算界广泛使用的Pareto最优性定义更完善，更适合于求解多目标优化问题。
In this paper the authors point out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance. The key contribution of this paper is the discovery of the new definition of optimality calledε-optimality for MOP that is based on a new conception, so calledε-dominance, which not only considers the difference of the number of superior and inferior objectives between two feasible solutions, but also considers the values of improved objective functions underlying the hypothesis that all objectives in the problem have equal importance. Two new evolutionary algorithms are given, whereε- dominance is used as a selection strategy with the winning score as an elite strategy for search -optimal solutions. Two benchmark problems are designed for testing the new concepts of many-objective optimization problems. Numerical experiments show that the new definition of optimality is more perfect than that of the Pareto Optimality which is widely used in the evolutionary computation community for solving many-objective optimization problems.