An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition

Abstract—Achieving balance between convergence and diver-sity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance-and decomposition-based approaches, for many-objective opti-mization. Our major purpose is to exploit the merits of both dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary process. The per-formance of our proposed method is validated and compared with four state-of-the-art algorithms on a number of unconstrained benchmark problems with up to 15 objectives. Empirical results fully demonstrate the superiority of our proposed method on all considered test instances. In addition, we extend this method to solve constrained problems having a large number of objectives. Compared to two other recently proposed constrained optimiz-ers, our proposed method shows highly competitive performance on all the constrained optimization problems. Index Terms—Constrained optimization, decomposition, evolu-tionary computation, many-objective optimization, Pareto opti-mality, steady state