A Parallel Co-evolutionary Metaheuristic

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.