A Many-Objective Evolutionary Algorithm Based on Dual Selection Strategy

In high-dimensional space, most multi-objective optimization algorithms encounter difficulties in solving many-objective problems because they cannot balance convergence and diversity. As the number of objectives increases, non-dominated solutions become difficult to distinguish while challenging assessment diversity objective space. To reduce selection pressure improve diversity, this article proposes a evolutionary algorithm based on dual strategy (MaOEA/DS). First, new distance function is designed as an effective metric. Then, function, point crowding-degree (PC) strategy, proposed further enhance algorithm’s ability superior population. Finally, proposed. first selection, individuals with best are selected from top few good population, focusing population convergence. second PC used select larger crowding values, emphasizing extensively evaluate performance algorithm, paper compares several state-of-the-art algorithms. The experimental results show that MaOEA/DS outperforms other comparison overall performance, indicating effectiveness algorithm.