A New Approach for the Solution of MultipleObjective Optimization Problems Based onReinforcement

Many problems can be characterized by several competing objectives. Multiple objective optimization problems have recently received considerable attention specially by the evolutionary algorithms community. Their proposals, however, require an adequate codiication of the problem into strings, which is not always easy to do. This paper introduces a new algorithm, called MDQL, for multiple objective optimization problems which does not suuer from previous limitations. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. Furthermore, an extension for applying reinforcement learning to continuos functions is also given. Successful results of MDQL on a continuos non restricted problem whose Pareto front is convex and on a continuos non-convex problem with restrictions are described.