Multiobjective evolutionary algorithm reinforcing specific objective

This paper proposes a multiobjective evolutionary algorithm (MOEA) for the problem with many objectives, where each objective is more strengthened. In the real world applications, satisfying as many objectives as possible somewhat at the same time can be less preferred than optimizing each specific objective individually. To solve this kind of problems, this paper proposes the complement of (1-k) dominance and the pruning method considering objective deviation to get a set of nondominated solutions with specifically optimized objectives. Promoting the specificity of objective improves the optimization performance on problems with many objectives. In experimental results, proposed algorithm shows improved performance compared with the state-of-the-art MOEAs such as SPEA, SPEA2 and NSGA2. The performance is measured in terms of the solution set coverage and the closeness to the true Pareto front. Also, diversity metric is applied to verify the spread of nondominated set.