Pareto memetic algorithm for multiple objective optimization with an industrial application |
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Authors: | Arnaud Zinflou Caroline Gagné Marc Gravel Wilson L. Price |
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Affiliation: | (1) Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555, Boul. de l’Université, Chicoutimi, PQ, Canada, G7H 2B1;(2) Faculté des Sciences de l’Administration, Université Laval, Ste-Foy, PQ, Canada, G1K 7P4 |
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Abstract: | Multiple objective combinatorial optimization problems are difficult to solve and often, exact algorithms are unable to produce optimal solutions. The development of multiple objective heuristics was inspired by the need to quickly produce acceptable solutions. In this paper, we present a new multiple objective Pareto memetic algorithm called PMSMO. The PMSMO algorithm incorporates an enhanced fine-grained fitness assignment, a double level archiving process and a local search procedure to improve performance. The performance of PMSMO is benchmarked against state-of-the-art algorithms using 0–1 multi-dimensional multiple objective knapsack problem from the literature and an industrial scheduling problem from the aluminum industry. |
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Keywords: | Memetic algorithms Multiple objectives Combinatorial optimization Niche Elitism |
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