A multi-agent based optimization method applied to the quadratic assignment problem

•MAOM is a multi-agent based and distributed method which is applied to QAP.•MAOM is composed of search agents for intensification and diversification.•MAOM uses a learning mechanism to coordinate the tasks of search agents.•A computational study is presented based on the set of 135 QAP instances.•The MAOM model could be adapted to solve other combinatorial search problems. Inspired by the idea of interacting intelligent agents of a multi-agent system, we introduce a multi-agent based optimization method applied to the quadratic assignment problem (MAOM-QAP). MAOM-QAP is composed of several agents (decision-maker agent, local search agents, crossover agents and perturbation agent) which are designed for the purpose of intensified and diversified search activities. With the help of a reinforcement learning mechanism, MAOM-QAP dynamically decides the most suitable agent to activate according to the state of search process. Under the coordination of the decision-maker agent, the other agents fulfill dedicated search tasks. The performance of the proposed approach is assessed on the set of well-known QAP benchmark instances, and compared with the most advanced QAP methods of the literature. The ideas proposed in this work are rather general and could be adapted to other optimization tasks. This work opens the way for designing new distributed intelligent systems for tackling other complex search problems.