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. (2009, March). An elitist GRASP metaheuristic for the multi-objective quadratic assignment problem. Presented at EMO: International Conference on Evolutionary Multi-Criterion Optimization, Nantes, France

• Presentation Conference Type: Edited Proceedings
• 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