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. © 2000 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bachelet, V., & Talbi, E. G. (2000). A parallel co-evolutionary metaheuristic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1800 LNCS, pp. 628–635). Springer Verlag. https://doi.org/10.1007/3-540-45591-4_85
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