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Test problems for multiple objective evolutionary algorithms

journal contribution
posted on 2008-10-01, 00:00 authored by P Cheng, Zili ZhangZili Zhang
Multiple objective evolutionary algorithms (MOEAs) need to be tested on scalable test problems known as multiple objective optimization problems (MOPs). This study analyzes MOPs in terms of the constraint condition, the uniform representation of the Pareto-optimal front and the ability to reach the true Pareto-optimal front. For each item, test problems were developed for each aspect based on the fast elitist non-dominated sorting genetic algorithm II (NSGA-II). The results show that these problems effectively test the algorithm's performance in all three aspects, especially the constraint condition.

History

Journal

Qinghua Daxue Xuebao/Journal of Tsinghua University

Volume

48

Pagination

1756-1761

Location

Beijing, China

ISSN

1000-0054

Language

eng

Publication classification

CN.1 Other journal article

Publisher

Qinghua Daxue Xuebao Bianjibu / Tsinghua University

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