Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches

Haitham Ahmed Seada; Mohamed Abouhawwash; Kalyanmoy Deb

doi:10.1145/2908961.2908991

Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches

Haitham Ahmed Seada
, Mohamed Abouhawwash
, Kalyanmoy Deb

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

In EMO diversity of the obtained solutions is an important factor, particularly for decision makers. NSGA-III is a recently proposed reference direction based algorithm that was shown to be successful up to as many as 15 objectives. In this study, we propose a diversity enhanced version of NSGA-III. Our algorithm augments NSGA-III with two types of local search. The first aims at finding the true extreme points of the Pareto front, while the second targets internal points. The two local search optimizers are carefully weaved into the fabric of NSGA-III niching procedure. The final algorithm maintains the total number of function evaluations to a minimum, enables using small population sizes, and achieves higher diversity without sacrificing convergence on a number of multi and many-objective problems.

Original language	English
Title of host publication	GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
Editors	Tobias Friedrich
Publisher	Association for Computing Machinery, Inc
Pages	77-78
Number of pages	2
ISBN (Electronic)	9781450343237
DOIs	https://doi.org/10.1145/2908961.2908991
State	Published - 20 Jul 2016
Externally published	Yes
Event	2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States Duration: 20 Jul 2016 → 24 Jul 2016

Publication series

Name	GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Conference

Conference	2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
Country/Territory	United States
City	Denver
Period	20/07/16 → 24/07/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Keywords

Extreme points
Local search
Multiobjective evolutionary optimization

ASJC Scopus subject areas

Software
Computer Science Applications
Computational Theory and Mathematics

Access to Document

10.1145/2908961.2908991

Cite this

Seada, H. A., Abouhawwash, M., & Deb, K. (2016). Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches. In T. Friedrich (Ed.), GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 77-78). (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2908961.2908991

Seada, Haitham Ahmed ; Abouhawwash, Mohamed ; Deb, Kalyanmoy. / Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches. GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. editor / Tobias Friedrich. Association for Computing Machinery, Inc, 2016. pp. 77-78 (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference).

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abstract = "In EMO diversity of the obtained solutions is an important factor, particularly for decision makers. NSGA-III is a recently proposed reference direction based algorithm that was shown to be successful up to as many as 15 objectives. In this study, we propose a diversity enhanced version of NSGA-III. Our algorithm augments NSGA-III with two types of local search. The first aims at finding the true extreme points of the Pareto front, while the second targets internal points. The two local search optimizers are carefully weaved into the fabric of NSGA-III niching procedure. The final algorithm maintains the total number of function evaluations to a minimum, enables using small population sizes, and achieves higher diversity without sacrificing convergence on a number of multi and many-objective problems.",

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Seada, HA, Abouhawwash, M & Deb, K 2016, Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches. in T Friedrich (ed.), GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, pp. 77-78, 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion, Denver, United States, 20/07/16. https://doi.org/10.1145/2908961.2908991

Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches. / Seada, Haitham Ahmed; Abouhawwash, Mohamed; Deb, Kalyanmoy.
GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. ed. / Tobias Friedrich. Association for Computing Machinery, Inc, 2016. p. 77-78 (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In EMO diversity of the obtained solutions is an important factor, particularly for decision makers. NSGA-III is a recently proposed reference direction based algorithm that was shown to be successful up to as many as 15 objectives. In this study, we propose a diversity enhanced version of NSGA-III. Our algorithm augments NSGA-III with two types of local search. The first aims at finding the true extreme points of the Pareto front, while the second targets internal points. The two local search optimizers are carefully weaved into the fabric of NSGA-III niching procedure. The final algorithm maintains the total number of function evaluations to a minimum, enables using small population sizes, and achieves higher diversity without sacrificing convergence on a number of multi and many-objective problems.

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Seada HA, Abouhawwash M, Deb K. Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches. In Friedrich T, editor, GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2016. p. 77-78. (GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference). doi: 10.1145/2908961.2908991

Towards a better diversity of evolutionary multi-criterion optimization algorithms using local searches

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this