Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE. 1. Introduction Optimization problems exist in all kinds of engineering and scientific areas. When there is more than one objective in an optimization problem, it is called a multiobjective optimization problem (MOP). Since these objectives are usually in conflict with each other, the goal of solving a MOP is to find a set of compromise solutions regarding all objectives rather than a best one as in single-objective optimization problems. The solutions of MOP, also called as the Pareto-optimal solutions, are optimal in the sense that there exist no other feasible solutions which would decrease some criteria without causing the increase of at least one other criterion. Evolutionary algorithm (EA) is an optimization algorithm based on the evolution of a population. As it can search for multiple solutions in parallel, it has gained great attention from researchers. In recent years, many excellent EAs [1–4] have been proposed to solve the MOPs efficiently and MOEA has been recognized as one of the best methods to solve the MOPs. Generally, there are two performance measures in evaluating the Pareto-optimal solutions obtained by MOEA. One is the convergence measurement, which evaluates the adjacent degree between the Pareto solutions and the true optimal front. Another one is the diversity measurement, which evaluates the distribution of solutions in the objective space. In order to achieve good performance, many excellent strategies and
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