A Multi-agent Metaheuristic Optimization Framework with Cooperation

This article address a Multiagent Metaheuristic Optimization Framework. In this proposal, each agent acts independently in the search space of a combinatorial optimization problem. The Framework allows the simultaneous execution of various agents, in a cooperative way. The coalition concept of cooperation is adopted. The agents have auto-learning abilities, based on reinforcement learning. The ability of cooperation and its influence on the quality of solutions provided by the agents are confirmed by performed experiments. In addition, experiments show that influence is greater when the number of agents is increased.