基于最优性能分数的大规模稀疏多目标优化算法
A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores
AI总结：
针对大规模稀疏多目标优化问题，提出进化算法。用新初始化方法生成反映变量重要性分数及初始掩码向量模板，引入计算变量变异概率新策略和基于帕累托引导正态分布的实变量优化，实验表明算法性能优越。
摘要：
大规模稀疏多目标优化问题（LSSMOPs）涉及大量决策变量和只有少数非零变量的帕累托最优解。随着决策变量数量增加，准确识别非零变量变得越来越具有挑战性，优化性能也受到不利影响。为了解决这些问题，本文提出了一种用于LSSMOPs的进化算法。具体来说，我们提出了一种新的初始化方法，能够生成准确反映变量重要性的分数，以及一个可以定位非零变量的初始掩码向量模板。这导致生成高质量的初始种群。此外，本文还介绍了一种计算每个变量变异概率的新策略，以及一种基于帕累托引导正态分布的实变量新颖优化方法，使种群能够避免陷入局部最优，并快速收敛到全局最优。来自八个基准问题和三个实际应用的实验结果表明，与现有算法相比，所提出的算法具有优越的性能。
英文摘要：
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.
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