In this work, we study partial neighborhood local search (PNLS) techniques, which consist of adaptive walks where moves are chosen in a random subset of the current solution neighborhood. PNLSs balance between intensification and diversification is mainly determined by its single parameter λ designing the subset size. We analyze and discuss three PNLSs variants, using the abstraction of several combinatorial optimization problems into fitness landscapes: NK landscapes, Unconstrained Binary Quadratic Programming, Flow-shop scheduling, and Quadratic Assignment. Our empirical study first analyses the structure of these landscapes through indicators. Then, we perform a parameter study of PNLSs for two computational budgets to study the impact of the sample size on the balance between intensification and diversification on different landscapes. Moreover, these experiments allow us to set an appropriate parameter value to compare the ability of PNLSs to reach good-quality solutions accurately. Finally, we compare PNLS variants with two classical metaheuristics, identifying links between landscape characteristics and algorithms behavior.

中文翻译：

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部分邻域本地搜索

在这项工作中，我们研究了部分邻域局部搜索 (PNLS) 技术，该技术由自适应游走组成，其中移动是在当前解决方案邻域的随机子集中选择的。PNLSs 在集约化和多样化之间的平衡主要由其设计子集大小的单个参数 λ 决定。我们分析和讨论了三个 PNLS 变体，将几个组合优化问题抽象为适应度环境：NK 环境、无约束二元二次规划、流水车间调度和二次分配。我们的实证研究首先通过指标分析这些景观的结构。然后，我们对两个计算预算的 PNLS 进行参数研究，以研究样本量对不同景观集约化和多样化之间平衡的影响。此外，这些实验允许我们设置适当的参数值来比较 PNLS 准确获得高质量解决方案的能力。最后，我们将 PNLS 变体与两种经典的元启发式方法进行比较，确定景观特征和算法行为之间的联系。