A many-objective evolutionary algorithm based on indicator selection and adaptive angle estimation

Currently, real-world complex engineering problems often have more than three objectives that need to be optimized simultaneously. Existing algorithms for solving them mostly rely on the shape of the Pareto front to maintain diversity, which makes it difficult to effectively balance the convergence and diversity of the solutions. To address the above problems, a many-objective evolutionary algorithm based on indicator selection and adaptive angle estimation (MaOEA-ISAE) is proposed in this paper. Firstly, an environmental selection strategy based on a unit vector indicator is presented to split the retention of dominant individuals into two phases to balance the diversity and convergence of the population. Then, to preserve the diversity of the population, an adaptive angle estimation strategy based on Pareto front curvature prediction is developed to select individuals with good diversity in the dominant region. Besides, the algorithm has a simple evolutionary process, and no additional parameters are involved. Finally, the proposed MaOEA-ISAE is compared with seven other representative many-objective evolutionary algorithms on test instances of 3–20 objectives with different Pareto front shapes and real-world water resource management problem. The experimental results show that the proposed algorithm has an overall win rate of about more than 60 %, indicating a strong competitiveness on problems with different Pareto fronts.