Artificial Immune System Approach for Multi Objective Optimization

This paper presents a modified Artificial Immune System based approach to solve multi objective optimization problems. The main objective of the solution of multi objective optimization problem is to help a human decision maker in taking his/her decision for finding the most preferred solution as the final result. This artificial immune system algorithm makes use of mechanism inspired by vertebrate immune system and clonal selection principle. In the present model crossover mechanism is integrated into traditional artificial immune system algorithm based on clonal selection theory. The Algorithm is proposed with real parameters value not binary coded parameters. Only non dominated individual and feasible best antibodies will add to the memory set. This algorithm will be used to solve various real life engineering multi-objective optimization problems. The attraction for choosing the artificial immune system to develop algorithm was that if an adaptive pool of antibodies can produce 'intelligent' behavior, we can use this power of computation to tackle the problem of multi objective optimization. Keywords: Artificial Immune System, Clonal Selection Theory, Multi Objective Optimization, Pareto Optimal