Agent Swarm Optimization: Exploding the search space

Abstract

Agent Swarm Optimization is a framework that combines the use of evolutionary algorithms, data mining, modeling and other techniques to find the best compromises among objectives in complex decision problems. It has been applied mainly in engineering cases where using classic optimization algorithms would require undesired simplifications of the problem or the use of simulators for evaluating the objective functions. The flexibility of evolutionary algorithms makes possible to use them in practically any case. Nevertheless, in this paper we are presenting a complex problem where using “pure” evolutionary algorithms was not resulting in good solutions. A different situation appeared after using rules for reducing the search space and moving the evolutionary process toward zones with a higher probability of containing good solutions. The results of using rules is also presented in this paper for the case studied. Additionally, the paper explores the capacity of the algorithms to discover additional rules that can improve the search process and the way the evolutionary algorithms behave in problems where the expert knowledge to generate search rules is limited.