Lanzhou University of Technology
The main goal of the multi-objective optimization algorithm is to achieve good diversity and convergence. In traditional high-dimensional multi-objective optimization algorithms, the selection operator is difficult to balance the convergence and diversity of the population. when the dimensionality of the objective increases. To solve this problem, this paper proposes a high-dimensional multi-objective algorithm named an indicator-based many-objective evolutionary algorithm with adaptive boundary selection. In environmental selection, first calculate the index of the two bodies in the population as the first selection criterion, and then propose an adaptive boundary selection strategy, which uses population evolution information to make fuzzy predictions of hyperplane coefficients, and then approximately Calculate the paradigm distance from the candidate individual to the hyperplane as the second selection criterion. Finally, the proposed algorithm is compared with four representative high-dimensional multi-objective algorithms. The experimental results show that when the algorithm handles the complex Pareto frontier high-dimensional multi-objective optimization problem, it can balance convergence and diversity while achieving better Maintain diversity.