Abstract. In this paper we address the problem of parallelizing local search. We propose a general framework where different local search engines cooperate (through communication) in the quest for a solution. Several parameters allow the user to instantiate and customize the framework, like the degree of intensification and diversification. We implemented a prototype in the X10 programming language based on the adaptive search method. We decided to use X10 in order to benefit from its ease of use and the architectural independence from parallel resources which it offers. Initial experiments prove the approach to be successful, as it outperforms previous systems as the number of processes increases.

Abstract. Several real-life applications can be stated in terms of the Quadratic Assignment Problem. Finding an optimal assignment is computationally very difficult, for many useful instances. We address this problem using a local search technique, based on Extremal Optimization and present experimental evidence that this approach is competitive. Moreover, cooperative parallel versions of our solver improve performance so much that large and hard instances can be solved quickly.

International audienceThe Quadratic Assignment Problem is at the core of several real-life applications. Finding an optimal assignment is computationally very difficult, for many useful instances. The best results are obtained with hybrid heuristics, which result in complex solvers. We propose an alternate solution where hybridization is obtain by means of parallelism and cooperation between simple single-heuristic solvers. We present experimental evidence that this approach is very efficient and can effectively solve a wide variety of hard problems, often surpassing state-of-the-art systems

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