标题： 在线聚类驱动的元启发式参数控制 显示英文标题

标题： Online Cluster-Based Parameter Control for Metaheuristic

摘要： 参数设置的概念是元启发式算法中至关重要且意义重大的过程，因为它可以极大地影响它们的性能。 这是一个高度复杂且具有挑战性的过程，因为它需要对优化算法和当前面临的优化问题有深入的理解。 近年来，自主决策系统的兴起吸引了在这个方向上持续的科学研究兴趣，利用了大量的参数调节方法。 有两种类型的方法：离线方法和在线方法。 在线方法通常在复杂的现实世界问题中表现出色，因为它们可以在算法执行过程中提供动态参数控制。 本研究提出了一种通用的在线参数调节方法，称为基于聚类的参数自适应（CPA），适用于基于群体的元启发式算法。 其主要思想在于识别参数搜索空间中的有希望区域，并在这些区域周围生成新的参数。 该方法的有效性已通过差分进化算法以及低维和高维问题的标准测试套件进行了验证。 获得的结果经过统计分析并与最先进的算法（包括先进的自动调优方法）进行了比较。 分析显示了CPA在各种基准问题和维度下的令人鼓舞的稳健性能。 显示英文摘要

摘要： The concept of parameter setting is a crucial and significant process in metaheuristics since it can majorly impact their performance. It is a highly complex and challenging procedure since it requires a deep understanding of the optimization algorithm and the optimization problem at hand. In recent years, the upcoming rise of autonomous decision systems has attracted ongoing scientific interest in this direction, utilizing a considerable number of parameter-tuning methods. There are two types of methods: offline and online. Online methods usually excel in complex real-world problems, as they can offer dynamic parameter control throughout the execution of the algorithm. The present work proposes a general-purpose online parameter-tuning method called Cluster-Based Parameter Adaptation (CPA) for population-based metaheuristics. The main idea lies in the identification of promising areas within the parameter search space and in the generation of new parameters around these areas. The method's validity has been demonstrated using the differential evolution algorithm and verified in established test suites of low- and high-dimensional problems. The obtained results are statistically analyzed and compared with state-of-the-art algorithms, including advanced auto-tuning approaches. The analysis reveals the promising solid CPA's performance as well as its robustness under a variety of benchmark problems and dimensions.