Constrained multi-objective optimization via neural network and cooperative populations

Abstract

Constrained multi-objective optimization problems are widely used in practical scenarios such as intelligent manufacturing and network communication. These problems are often made intractable by constraints, and achieving a balance between convergence, diversity, and feasibility becomes increasingly challenging. To address this issue, a constrained multi-objective evolutionary algorithm named NNCP is proposed, which is based on the neural network and, three cooperative populations. Specifically, the neural network is employed to accelerate the population’s convergence by utilizing neuron weights to capture neighborhood information. Among the three populations, the first population uses self-organizing mapping and curvature estimation to approximate the Pareto front, the second population utilizes non-dominance sorting and an angle selection mechanism to identify high-quality infeasible solutions, thereby enhancing diversity, and the third population adopts an adaptive penalty mechanism to improve feasibility. These populations work cooperatively to identify promising infeasible solutions and navigate infeasible regions to approximate the Pareto front. Finally, five state-of-the-art constrained multi-objective optimization algorithms are compared with NNCP. Out of the total 47 test problems, NNCP outperforms the best-performing baseline algorithm on more than 35 problems, highlighting its superior convergence and diversity capabilities.