Abstract
In order to show that the parallel co-evolution of different heuristic methods may lead to an efficient search strategy, we have hybridized three heuristic agents of complementary behaviours: A Tabu Search is used as the main search algorithm, a Genetic Algorithm is in charge with the diversification and a Kick Operator is applied to intensify the search. The three agents run simultaneously, they communicate and cooperate via an adaptive memory which contains a history of the search already done, focusing on high quality regions of the search space. This paper presents CO-SEARCH, the co-evolving heuristic we have designed, and its application on large scale instances of the quadratic assignment problem. The evaluations have been executed on large scale network of workstations via a parallel environment which supports fault tolerance and adaptive dynamic scheduling of tasks.
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© 2000 Springer-Verlag Berlin Heidelberg
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Bachelet, V., Talbi, EG. (2000). A Parallel Co-evolutionary Metaheuristic. In: Rolim, J. (eds) Parallel and Distributed Processing. IPDPS 2000. Lecture Notes in Computer Science, vol 1800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45591-4_85
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DOI: https://doi.org/10.1007/3-540-45591-4_85
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