A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores
For researchers and practitioners dealing with large-scale sparse multiobjective optimization, this work provides a more effective algorithm that addresses key challenges in variable identification and convergence.
This paper proposes an evolutionary algorithm for large-scale sparse multiobjective optimization problems (LSSMOPs) that uses a new initialization method and mutation strategy to improve identification of nonzero variables and optimization performance. The algorithm outperforms state-of-the-art methods on eight benchmarks and three real-world applications.
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.