Journal
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
Volume 19, Issue 4, Pages 495-520Publisher
WILEY
DOI: 10.1111/j.1475-3995.2011.00840.x
Keywords
multiple objective programming; knapsack problems; metaheuristics; local search
Funding
- Fonds National de la Recherche Scientifique
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The knapsack problem (KP) and its multidimensional version (MKP) are basic problems in combinatorial optimization. In this paper, we consider their multiobjective extension (MOKP and MOMKP), for which the aim is to obtain or approximate the set of efficient solutions. In the first step, we classify and briefly describe the existing works that are essentially based on the use of metaheuristics. In the second step, we propose the adaptation of the two-phase Pareto local search (2PPLS) to the resolution of the MOMKP. With this aim, we use a very large scale neighborhood in the second phase of the method, that is the PLS. We compare our results with state-of-the-art results and show that the results we obtained were never reached before by heuristics for biobjective instances. Finally, we consider the extension to three-objective instances.
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