Hui Li
An elitist GRASP metaheuristic for the multi-objective quadratic assignment problem
Li, Hui; Landa-Silva, Dario
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 |
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