Study on Pareto front of multi-objective optimization using immune algorithm

In this paper, a novel multi-objective optimization method based on immune algorithm is proposed, in which, not only the Pareto non-domination ranking scheme and the relative weight of objective function are not needed, but also the concordance set to determine whether each given solution is feasible or not. Instead, it uses a new comparison mechanism for individual´s fitness ranking procedure based on its Pareto fitting ratio. By using immune network metaphor and optimum maintaining strategy, a new real-coded immune algorithm is developed. The approach is tested with three benchmark functions and the results demonstrate the good performance of the approach in solving Pareto-optimal front (POF) of multi-objective optimization (MOP).