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An elitist GRASP metaheuristic for the multi-objective quadratic assignment problem

Li, Hui; Landa-Silva, Dario

Authors

Hui Li

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DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation



Abstract

We propose an elitist Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm, called mGRASP/MH, for approximating the Pareto-optimal front in the multi-objective quadratic assignment problem (mQAP). The proposed algorithm is characterized by three features: elite greedy randomized construction, adaptation of search directions and cooperation between solutions. The approach builds starting solutions in a greedy fashion by using problem-specific information and elite solutions found previously. Also, mGRASP/MH maintains a population of solutions, each associated with a search direction (i.e. weight vector). These search directions are adaptively changed during the search. Moreover, a cooperation mechanism is also implemented between the solutions found by different local search procedures in mGRASP/MH. Our experiments show thatmGRASP/MH performs better or similarly to several other state-of-the-art multi-objective metaheuristic algorithms when solving benchmark mQAP instances. © Springer-Verlag 2009.

Citation

Li, H., & Landa-Silva, D. (2010). An elitist GRASP metaheuristic for the multi-objective quadratic assignment problem. In Evolutionary Multi-Criterion Optimization: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009. Proceedings, (481-494). https://doi.org/10.1007/978-3-642-01020-0_38

Conference Name EMO: International Conference on Evolutionary Multi-Criterion Optimization
Start Date Mar 7, 2009
End Date Mar 10, 2009
Publication Date Dec 1, 2010
Deposit Date Feb 10, 2020
Publisher Springer Verlag
Volume 5467
Pages 481-494
Series Title Lecture Notes in Computer Science
Series Number 5467
Book Title Evolutionary Multi-Criterion Optimization: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009. Proceedings
ISBN 978-3-642-01019-4
DOI https://doi.org/10.1007/978-3-642-01020-0_38
Public URL https://nottingham-repository.worktribe.com/output/3088107
Publisher URL https://link.springer.com/chapter/10.1007%2F978-3-642-01020-0_38