A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems

Pedro Gómez-Meneses; Marcus Randall; Andrew Lewis

doi:10.1109/CEC.2010.5586194

A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems

Pedro Gómez-Meneses^*, Marcus Randall, Andrew Lewis

^*Corresponding author for this work

Bond Business School

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

8 Citations (Scopus)

Abstract

Extremal optimisation (EO) is a relatively recent nature-inspired heuristic whose search method is especially suitable to solve combinatorial optimisation problems. To date, most of the research in EO has been applied for solving single-objective problems and only a relatively small number of attempts to extend EO toward multi-objective problems. This paper presents a hybrid multi-objective version of EO (HMEO) to solve multi-objective combinatorial problems. This new approach consists of a multi-objective EO framework, for the coarse-grain search, which contains a novel multi-objective combinatorial local search framework for the fine-grain search. The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem. The results show that the new algorithm is able to obtain competitive results to SPEA2 and NSGA-II. The non-dominated points found are well-distributed and similar or very close to the Pareto-front found by previous works.

Original language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs	https://doi.org/10.1109/CEC.2010.5586194
Publication status	Published - 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010

Conference

Conference	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Country/Territory	Spain
City	Barcelona
Period	18/07/10 → 23/07/10

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10.1109/CEC.2010.5586194

Cite this

@inproceedings{eee40f10c7164344a446f9d264141e5f,

title = "A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems",

abstract = "Extremal optimisation (EO) is a relatively recent nature-inspired heuristic whose search method is especially suitable to solve combinatorial optimisation problems. To date, most of the research in EO has been applied for solving single-objective problems and only a relatively small number of attempts to extend EO toward multi-objective problems. This paper presents a hybrid multi-objective version of EO (HMEO) to solve multi-objective combinatorial problems. This new approach consists of a multi-objective EO framework, for the coarse-grain search, which contains a novel multi-objective combinatorial local search framework for the fine-grain search. The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem. The results show that the new algorithm is able to obtain competitive results to SPEA2 and NSGA-II. The non-dominated points found are well-distributed and similar or very close to the Pareto-front found by previous works.",

author = "Pedro G{\'o}mez-Meneses and Marcus Randall and Andrew Lewis",

year = "2010",

doi = "10.1109/CEC.2010.5586194",

language = "English",

isbn = "9781424469109",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

}

Gómez-Meneses, P, Randall, M & Lewis, A 2010, A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010., 5586194, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18/07/10. https://doi.org/10.1109/CEC.2010.5586194

A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems. / Gómez-Meneses, Pedro; Randall, Marcus; Lewis, Andrew.
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5586194.

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

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T1 - A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems

AU - Gómez-Meneses, Pedro

AU - Randall, Marcus

AU - Lewis, Andrew

PY - 2010

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N2 - Extremal optimisation (EO) is a relatively recent nature-inspired heuristic whose search method is especially suitable to solve combinatorial optimisation problems. To date, most of the research in EO has been applied for solving single-objective problems and only a relatively small number of attempts to extend EO toward multi-objective problems. This paper presents a hybrid multi-objective version of EO (HMEO) to solve multi-objective combinatorial problems. This new approach consists of a multi-objective EO framework, for the coarse-grain search, which contains a novel multi-objective combinatorial local search framework for the fine-grain search. The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem. The results show that the new algorithm is able to obtain competitive results to SPEA2 and NSGA-II. The non-dominated points found are well-distributed and similar or very close to the Pareto-front found by previous works.

AB - Extremal optimisation (EO) is a relatively recent nature-inspired heuristic whose search method is especially suitable to solve combinatorial optimisation problems. To date, most of the research in EO has been applied for solving single-objective problems and only a relatively small number of attempts to extend EO toward multi-objective problems. This paper presents a hybrid multi-objective version of EO (HMEO) to solve multi-objective combinatorial problems. This new approach consists of a multi-objective EO framework, for the coarse-grain search, which contains a novel multi-objective combinatorial local search framework for the fine-grain search. The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem. The results show that the new algorithm is able to obtain competitive results to SPEA2 and NSGA-II. The non-dominated points found are well-distributed and similar or very close to the Pareto-front found by previous works.

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M3 - Conference contribution

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SN - 9781424469109

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

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Y2 - 18 July 2010 through 23 July 2010

ER -

A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems

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