A novel multiobjective evolution strategy: design for adaptive balance between proximity and diversity

This paper proposes a new multiobjective evolutionary approach to investigate the adaptive balance between proximity and diversity. The proposed algorithm combines several elements such as Gaussian and Cauchy mutations, a nondominance selection, and a dynamic external archive. Numerical experimentations are presented using three benchmark instances, and results are compared with three state-of-the-art algorithms. It is drawn that our algorithm is superior to some extent in term of finding a near-optimal, well-extended and uniformly diversified Pareto optimal front.