Bandit-inspired memetic algorithms for solving quadratic assignment problems

Fr. Puglierin; M.M. Drugan; M.A. Wiering

doi:10.1109/CEC.2013.6557814

Bandit-inspired memetic algorithms for solving quadratic assignment problems

Fr. Puglierin, M.M. Drugan, M.A. Wiering

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

6 Citaten (Scopus)

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In this paper we propose a novel algorithm called the Bandit-Inspired Memetic Algorithm (BIMA) and we have applied it to solve different large instances of the Quadratic Assignment Problem (QAP). Like other memetic algorithms, BIMA makes use of local search and a population of solutions. The novelty lies in the use of multi-armed bandit algorithms and assignment matrices for generating novel solutions, which will then be brought to a local minimum by local search. We have compared BIMA to multi-start local search (MLS) and iterated local search (ILS) on five QAP instances, and the results show that BIMA significantly outperforms these competitors.

Originele taal-2	Engels
Titel	2013 IEEE Congress on Evolutionary Computation (CEC), 20-23 June 2013, Cancun, Mexico
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	2078-2085
ISBN van geprinte versie	978-1-4799-0453-2
DOI's	https://doi.org/10.1109/CEC.2013.6557814
Status	Gepubliceerd - 2013
Extern gepubliceerd	Ja

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10.1109/CEC.2013.6557814

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@inproceedings{0d6897ab9d4545bbb79f04db0e28c9af,

title = "Bandit-inspired memetic algorithms for solving quadratic assignment problems",

abstract = "In this paper we propose a novel algorithm called the Bandit-Inspired Memetic Algorithm (BIMA) and we have applied it to solve different large instances of the Quadratic Assignment Problem (QAP). Like other memetic algorithms, BIMA makes use of local search and a population of solutions. The novelty lies in the use of multi-armed bandit algorithms and assignment matrices for generating novel solutions, which will then be brought to a local minimum by local search. We have compared BIMA to multi-start local search (MLS) and iterated local search (ILS) on five QAP instances, and the results show that BIMA significantly outperforms these competitors.",

keywords = "Meta-heuristics, Memetic Algorithms, Combinatorial Optimization, Quadratic Assignment Problem, Multiarmed Bandit Algorithms",

author = "Fr. Puglierin and M.M. Drugan and M.A. Wiering",

year = "2013",

doi = "10.1109/CEC.2013.6557814",

language = "English",

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Bandit-inspired memetic algorithms for solving quadratic assignment problems. / Puglierin, Fr.; Drugan, M.M.; Wiering, M.A.
2013 IEEE Congress on Evolutionary Computation (CEC), 20-23 June 2013, Cancun, Mexico. Piscataway: Institute of Electrical and Electronics Engineers, 2013. blz. 2078-2085.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - Wiering, M.A.

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Y1 - 2013

N2 - In this paper we propose a novel algorithm called the Bandit-Inspired Memetic Algorithm (BIMA) and we have applied it to solve different large instances of the Quadratic Assignment Problem (QAP). Like other memetic algorithms, BIMA makes use of local search and a population of solutions. The novelty lies in the use of multi-armed bandit algorithms and assignment matrices for generating novel solutions, which will then be brought to a local minimum by local search. We have compared BIMA to multi-start local search (MLS) and iterated local search (ILS) on five QAP instances, and the results show that BIMA significantly outperforms these competitors.

AB - In this paper we propose a novel algorithm called the Bandit-Inspired Memetic Algorithm (BIMA) and we have applied it to solve different large instances of the Quadratic Assignment Problem (QAP). Like other memetic algorithms, BIMA makes use of local search and a population of solutions. The novelty lies in the use of multi-armed bandit algorithms and assignment matrices for generating novel solutions, which will then be brought to a local minimum by local search. We have compared BIMA to multi-start local search (MLS) and iterated local search (ILS) on five QAP instances, and the results show that BIMA significantly outperforms these competitors.

KW - Meta-heuristics

KW - Memetic Algorithms

KW - Combinatorial Optimization

KW - Quadratic Assignment Problem

KW - Multiarmed Bandit Algorithms

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DO - 10.1109/CEC.2013.6557814

M3 - Conference contribution

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BT - 2013 IEEE Congress on Evolutionary Computation (CEC), 20-23 June 2013, Cancun, Mexico

PB - Institute of Electrical and Electronics Engineers

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ER -

Bandit-inspired memetic algorithms for solving quadratic assignment problems

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