GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization

摘要

In this paper, we propose a new Pareto generic algorithm, called CISMOO, which hybridizes genetic algorithm and artificial immune systems. GISMOO algorithm is generic in the sense that it can be used to solve both combinatorial and continuous optimization problems. The proposed approach offers an original iterative process in two phases: a Genetic Phase and an Immune Phase. The Immune Phase is used to identify and to emphasize the solutions located in less crowded regions found during the iterative process of the algorithm. Simulation results on difficult test problems, both in combinatorial and continuous optimization, show that the proposed approach, in most problems, is able to obtain better results than state of the art algorithms.