A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores
2026-07-06 • Neural and Evolutionary Computing
Neural and Evolutionary Computing
AI summaryⓘ
The authors address large-scale optimization problems where only a few decision variables matter. They created a new method that starts with a better guess of which variables are important and designed a way to change variables smartly during the search. This helps their algorithm avoid getting stuck and find good solutions faster. Testing on benchmarks and real cases showed their method works better than existing ones.
Large-scale sparse multiobjective optimizationEvolutionary algorithmPareto optimal solutionsInitialization methodMutation probabilityPareto-guided normal distributionLocal optimaBenchmark problemsReal-world applications
Authors
Jia-Lin Mai, Min-Rong Chen, Guo-Qiang Zeng, Xiang Liu, Jian Weng
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.