A coordinated many-objective evolutionary algorithm using random adaptive parameters

Selection strategy is an essential evolutionary component for many-objective evolutionary algorithms, including mating selection and environmental selection. However, there are still many challenges in evolution as the number of objectives increases, such as the conflict between diversity and convergence, and insufficient Pareto selection pressure. To address these problems, this paper proposes a coordinated many-objective evolutionary algorithm using random adaptive parameters (MaOEA-CO). Specifically, the algorithm adopts the coordinated selection mechanism as a new mating selection strategy that regulates the diversity and convergence weight of individuals through random adaptive parameters design, which can better balance the diversity and convergence of individuals at the edge of the Pareto front. Moreover, an environmental selection method based on coordination angle and Pareto distance indicators is designed. The angle indicator selects the two less diverse individuals from the whole population, and the Pareto distance indicator is used to remove the poor convergence individuals. We are ensuring population diversity while improving the selection pressure of the algorithm. The results of comparative experiments conducted on the standard test suite and Wilcoxon demonstrate the superiority of the MaOEA-CO algorithm in comparison with six state-of-the-art designs in terms of solution quality and computational efficiency. Besides, a many-objective coal model is applied to verify the performance of the MaOEA-CO algorithm further. The algorithm provides a better Pareto solution and promotes the development of coal enterprises.