An Optimality Theory Based Proximity Measure for Evolutionary Multi-Objective and Many-Objective Optimization

被引：16
|
作者：
Deb, Kalyanmoy [1 ]
Abouhawwash, Mohamed [1 ]
Dutta, Joydeep [2 ]
机构：
[1] Michigan State Univ, Computat Optimizat & Innovat COIN Lab, E Lansing, MI 48824 USA
[2] Indian Inst Technol, Dept Humanities & Social Sci, Kanpur 208016, Uttar Pradesh, India
来源：
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT II | 2015年 / 9019卷
关键词：
Multi-objective optimization; Evolutionary optimization; Termination criterion; KkT optimality conditions; KKT POINTS; ALGORITHM;
D O I：
10.1007/978-3-319-15892-1_2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Evolutionary multi- and many-objective optimization (EMO) methods attempt to find a set of Pareto-optimal solutions, instead of a single optimal solution. To evaluate these algorithms, performance metrics either require the knowledge of the true Pareto-optimal solutions or, are ad-hoc and heuristic based. In this paper, we suggest a KKT proximity measure (KKTPM) that can provide an estimate of the proximity of a set of trade-off solutions from the true Pareto-optimal solutions. Besides theoretical results, the proposed KKT proximity measure is computed for iteration-wise trade-off solutions obtained from specific EMO algorithms on two, three, five and 10-objective optimization problems. Results amply indicate the usefulness of the proposed KKTPM as a termination criterion for an EMO algorithm.
引用
收藏
页码：18 / 33
页数：16
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