Effects of Cooperation in a Bioinspired Multi-agent Autonomous System for Solving Optimization Problems

Bimasco - Bioinspired Multi-Agent System for Combinatorial Optimization - consists of an autonomous multi-agent system for solving optimization problems of different classes. This system uses the metaphor of artificial life in which the artificial world represents the search space of a problem, populated by a set of feasible solutions of the problem and grouped into inanimate entities, called regions. Similarly, the world is inhabited by animated entities, agents, each encapsulating one metaheuristic. In this context, this paper introduces an asynchronous and non-deterministic model for the dynamics of interactions among agents and regions, so that it operates as a self-organizing discrete dynamical system. Computational experiments were performed using different classes of combinatorial as well as non-combinatorial optimization problems, including one problem involving a function, which is usually used as benchmark for continuous optimization methods and the knapsack problem. The preliminary results thus obtained show that the dynamics of the implemented model is most effective when there is cooperation between the agents, due to the learning process that occurs from the actions and interactions among them.

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