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

页码：18 / 33

页数：16

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