Many-objective evolutionary algorithm assisted by a novel angle-based fitness strategy

Measuring the convergence of solutions is greatly significant for many-objective optimization problems. Although various methods have been proposed regarding this issue, they still suffer from the performance consistency on different problems. For this issue, this article proposes a novel angle-based fitness strategy. To be specific, the convergence of solutions is defined with adaptive key solutions, which are determined by the angles between solutions. Based on the novel angle-based fitness strategy, this article designs a many-objective evolutionary algorithm assisted by a novel angle-based fitness strategy. Experiments are conduced to verify the performance of the proposed method in comparison with other state-of-the-art methods. The experimental analyzes illustrate the outstanding performance of the proposed method for many-objective optimization problems.