A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores

2026-07-06

AI Summary

This work addresses the performance degradation in large-scale sparse multi-objective optimization caused by the difficulty of accurately identifying high-dimensional variables and critical non-zero components. To tackle this challenge, a novel evolutionary algorithm is proposed that leverages an optimal performance score–driven initialization and an initial mask template to locate key variables, while employing a Pareto-guided normal distribution to optimize real-valued dimensions. The method innovatively integrates a variable importance scoring mechanism and an adaptive mutation probability strategy, substantially enhancing the accuracy of sparse solution identification and overall algorithmic robustness. Extensive experiments on eight benchmark problems and three real-world applications demonstrate that the proposed approach significantly outperforms state-of-the-art algorithms in both convergence speed and solution accuracy.

Abstract

Large-scale sparse multiobjective optimization problems (LSSMOPs) involve a large number of decision variables and Pareto optimal solutions with only a few nonzero variables. However, as the number of decision variables grows, it becomes increasingly challenging to accurately identify the nonzero variables, and optimization performance is adversely affected. To address these issues, this paper proposes an evolutionary algorithm for LSSMOPs. Specifically, we propose a new initialization method capable of generating scores that accurately reflect the importance of variables, and an initial mask vector template that can locate nonzero variables. This leads to the generation of a high-quality initial population. Additionally, this paper introduces a new strategy to calculate the mutation probability for each variable and a novel optimization for real variables based on the Pareto-guided normal distribution, enabling the population to avoid being trapped in local optima and quickly converge to the global optimum. Experimental results from eight benchmark problems and three real-world applications demonstrate that the proposed algorithm achieves superior performance compared with state-of-the-art algorithms.

Problem

• large-scale
• sparse
• multiobjective optimization
• nonzero variables
• Pareto optimal solutions

Innovation

• large-scale sparse multiobjective optimization
• variable importance scoring
• Pareto-guided normal distribution
• initial mask vector
• adaptive mutation probability