Abstract
Multiple-objective metaheuristics, e.g., multiple-objective evolutionary algorithms, constitute one of the most active fields of multiple-objective optimization. Since 1985, a significant number of different methods have been proposed. However, only few comparative studies of the methods were performed on large-scale problems. We continue two comparative experiments on the multiple-objective 0/1 knapsack problem reported in the literature. We compare the performance of two multiple-objective genetic local search (MOGLS) algorithms to the best performers in the previous experiments using the same test instances. The results of our experiment indicate that our MOGLS algorithm generates better approximations to the nondominated set in the same number of functions evaluations than the other algorithms.
Keywords
Knapsack problemContinuous knapsack problemMetaheuristicMathematical optimizationLocal search (optimization)MathematicsGenetic algorithmSet (abstract data type)Evolutionary algorithmComputer science
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 6
- Issue
- 4
- Pages
- 402-412
- Citations
- 465
- Access
- Closed
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Cite This
Andrzej Jaszkiewicz
(2002).
On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment.
IEEE Transactions on Evolutionary Computation
, 6
(4)
, 402-412.
https://doi.org/10.1109/tevc.2002.802873
Identifiers
- DOI
- 10.1109/tevc.2002.802873