Hypervolume-Based Local Search in Multi-Objective Evolutionary Optimization

被引：11

作者：

Pilat, Martin ^{[1
]}

Neruda, Roman ^{[2
]}

机构：

[1] Charles Univ Prague, Fac Math & Phys, Prague 11801, Czech Republic

[2] Acad Sci Czech Republ, Inst Comp Sci, Prague 18207, Czech Republic

来源：

GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2014年

关键词：

Multi-objective optimization; surrogate modeling; NSGA-II; hyper-volume; Algorithms;

D O I：

10.1145/2576768.2598332

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes a surrogate based multi-objective evolutionary algorithm with hyper-volume contribution-based local search. The algorithm switches between an NSGA-II phase and a local search phase. In the local search phase, a model for each of the objectives is trained and CMAES is used to optimize the hyper-volume contribution of each individual with respect to its two neighbors on the non-dominated front. The performance of the algorithm is evaluated using the well known ZDT and WFG benchmark suites.

引用

页码：637 / 644

页数：8

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