Abstract
Proposes a hybrid algorithm for finding a set of non-dominated solutions of a multi-objective optimization problem. In the proposed algorithm, a local search procedure is applied to each solution (i.e. to each individual) generated by genetic operations. The aim of the proposed algorithm is not to determine a single final solution but to try to find all the non-dominated solutions of a multi-objective optimization problem. The choice of the final solution is left to the decision maker's preference. The high searching ability of the proposed algorithm is demonstrated by computer simulations on flowshop scheduling problems.
Keywords
Mathematical optimizationComputer scienceGenetic algorithmDecision makerSet (abstract data type)Local search (optimization)AlgorithmPreferenceJob shop schedulingOptimization problemPopulation-based incremental learningScheduling (production processes)MathematicsOperations research
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 119-124
- Citations
- 251
- Access
- Closed
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Cite This
Hisao Ishibuchi,
Tadahiko Murata
(2002).
Multi-objective genetic local search algorithm.
, 119-124.
https://doi.org/10.1109/icec.1996.542345
Identifiers
- DOI
- 10.1109/icec.1996.542345