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

TLDR

Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization, and existing methods that work well for two or three objectives struggle with many-objective problems. This paper proposes a unified paradigm that combines dominance- and decomposition-based approaches to balance convergence and diversity in many-objective optimization. The method is validated against four state-of-the-art algorithms on unconstrained benchmark problems with up to 15 objectives and extended to handle constrained problems with many objectives. Empirical results show the proposed method outperforms all considered test instances and achieves highly competitive performance on constrained problems compared to two recent optimizers.

Abstract

Achieving balance between convergence and diversity 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 optimization. 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 performance 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 optimizers, our proposed method shows highly competitive performance on all the constrained optimization problems.