In multi-objective optimization the objective is to reach a point which is Pareto efficient. However we usually encounter many such points and choosing a point amongst them possesses another problem. In many applications we are required to choose a point having a good spread over all objective functions which is a direct consequence of the notion of fairness. In this paper we propose two things...

In this paper, we show how cellular structures can be combined with a multi-objective genetic algorithm (MOGA) for improving its search ability to find Pareto-optimal solutions of multi-objective optimization problems. We propose an assignment method of a different search direction to each cell for implementing a cellular MOGA. In our cellular MOGA, every individual in each population exists in...

In this paper, we propose a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts. The primary design goal of the proposed approach is to reduce the total number of objective function evaluations required to produce a reasonably good approximation of the true Pareto front of a problem. The main idea of the proposed approach is to concentrate the search effort on ...