Tabu-based exploratory evolutionary algorithm for effective multi-objective optimization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
Subtitle of host publicationFirst International Conference, EMO 2001 Zurich, Switzerland, March 7–9, 2001 Proceedings
EditorsEckart Zitzler, Lothar Thiele, Kalyanmoy Deb, Carlos Artemio Coello Coello, David Corne
PublisherSpringer Berlin Heidelberg
Pages344-358
ISBN (Electronic)978-3-540-44719-1
ISBN (Print)978-3-540-41745-3
Publication statusPublished - Mar 2001
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1993
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title1st International Conference on Evolutionary Multi-Criterion Optimization, EMO 2001
PlaceSwitzerland
CityZurich
Period7 - 9 March 2001

Abstract

This paper proposes an exploratory multi-objective evolutionary algorithm (EMOEA) that makes use of the integrated features of tabu search and evolutionary algorithms for effective multi-objective optimization. It incorporates a tabu list and tabu constraint for individual examination and preservation to enhance the evolutionary search diversity in multi-objective optimization, which subsequently helps to avoid the search from trapping in local optima and at the same time, promotes the evolution towards the global Pareto-front. A novel method of lateral interference is also suggested, which is capable of distributing non-dominated individuals uniformly along the discovered Pareto-front at each generation. Unlike existing niching/sharing methods, lateral interference can be performed without the need of any parameter setting and can be flexibly applied in either parameter or objective domain depending on the nature of the optimization problem involved. The proposed features are experimented in order to illustrate their behavior and usefulness in the algorithm.

Citation Format(s)

Tabu-based exploratory evolutionary algorithm for effective multi-objective optimization. / Khor, E. F.; Tan, K. C.; Lee, T. H.

Evolutionary Multi-Criterion Optimization: First International Conference, EMO 2001 Zurich, Switzerland, March 7–9, 2001 Proceedings. ed. / Eckart Zitzler; Lothar Thiele; Kalyanmoy Deb; Carlos Artemio Coello Coello; David Corne. Springer Berlin Heidelberg, 2001. p. 344-358 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1993).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)