In the last two decades, a variety of different types of multiobjective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large. Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs. The experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs.

中文翻译：

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一种大规模稀疏多目标优化问题的进化算法

在过去的二十年中，进化计算社区对各种不同类型的多目标优化问题 (MOP) 进行了广泛的研究。然而，大多数现有的进化算法在处理帕累托最优解稀疏（即最优解的大多数决策变量为零）的MOPs时遇到困难，尤其是当决策变量数量很大时。这种大规模的稀疏 MOP 存在于广泛的应用中，例如，旨在从大量候选特征中找到一小部分特征的特征选择，或连接稀疏以减轻过度拟合的神经网络的结构优化。本文提出了一种用于解决大规模稀疏 MOP 的进化算法。所提出的算法通过考虑帕累托最优解的稀疏性，提出了一种新的种群初始化策略和遗传算子，以保证生成解的稀疏性。此外，本文还设计了一个测试套件来评估所提出算法对大规模稀疏 MOP 的性能。所提出的测试套件和四个应用示例的实验结果证明了所提出的算法在解决大规模稀疏 MOP 方面优于七种现有算法。