A Cα -dominance-based solution estimation evolutionary algorithm for many-objective optimization

Balancing convergence and diversity is a key issue for many-objective optimization problems (MaOPs), which is a great challenge to the classical Pareto-based multi-objective algorithms due to its severe lack of selection pressure. To relieve the above challenge, a C alpha-dominance-based solution estimation evolutionary algorithm is proposed for MaOPs. In the proposed algorithm, a new dominance method, called C alpha-dominance, is proposed to provide reasonable selection pressure for MaOPs. By designing a nonlinear function to transform the original objectives, C alpha-dominance expands the dominated area where dominance resistant solutions located, while remains the solutions to be non-dominated in area close to Pareto optimal solutions. Furthermore, an adaptive parameter adjustment mechanism on the unique parameter alpha of C alpha-dominance is designed to control the expansion degree of the dominance area based on the number of objectives and the stages of evolution. Finally, a new solution estimation scheme based on C alpha-dominance is designed to evaluate the quality of each solution, which incorporates convergence information and diversity information of each solution. The experimental results on widely used benchmark problems having 5-20 objectives have shown the proposed algorithm is more effective in terms of both convergence enhancement and diversity maintenance. (C)& nbsp;2022 Elsevier B.V. All rights reserved.