🤖 AI Summary
Parameter tuning for metaheuristic algorithms is notoriously complex and highly problem-dependent; existing online tuning methods suffer from limited dynamism and poor generalizability. To address this, we propose Clustering-based Parameter Adaptation (CPA), the first online parameter control framework for population-based algorithms that integrates unsupervised clustering into real-time parameter adaptation—requiring no prior knowledge, automatically identifying high-performing regions in parameter space, and generating nearby candidate parameters to enable structured exploration and adaptive evolution. CPA is embedded within a differential evolution framework and augmented with statistical significance testing. Comprehensive evaluation across high- and low-dimensional benchmark suites demonstrates that CPA significantly outperforms state-of-the-art automated parameter tuning methods in convergence speed, solution stability, and cross-dimensional generalizability, while exhibiting strong robustness and broad applicability across diverse optimization problems.
📝 Abstract
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.