Scored Pareto MEC for Multi-Objective Optimization and Its Convergence
Xinling Zhou, Chengyi Sun, Xunzhang Gao · 2006
In this paper, a new evolutionary optimization algorithm named Scored Pareto Mind Evolutionary Computation (SP-MEC) is proposed, which embeds the theory of Pareto and information of density into the Mind Evolutionary Computation (MEC) in order to deal with multi-objective optimization problems. Taking advantage of two unique operations, similartaxis and dissimilation, the MEC is an efficient optimization algorithm combining the global search with local search. Thus SP-MEC can further effectively converge to the Pareto front, and achieve the high-quality trade-off front for multi-objective optimization. The features of the proposed SP-MEC are the employments of the relation of Pareto and density information of individuals. Therefore, the optimal solutions acquired by our SP-MEC distribute uniformly on the Pareto front. The feasibility and efficiency of this SP-MEC are demonstrated using numerical examples. The convergence of the sequence of populations generated from the similartaxis operation is also analyzed under certain conditions.