用聚类方法动态可视化优化算法搜索过程，揭示其稳定性与差异。
ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
- 通过聚类追踪算法迭代中解的演化轨迹
- 提出稳定性和相似性度量，量化算法行为一致性
- 适用于研究优化算法设计与比较的科研人员
理解数值元启发式优化算法的行为对推动其发展和应用至关重要。传统可视化方法如收敛图、轨迹映射和适应度景观分析，在高维或复杂解空间中难以展现搜索过程的结构动态。为此，我们提出一种新的表示与可视化方法：对算法探索的解候选进行聚类，并跟踪各迭代中聚类成员的变化，从而提供动态可解释的搜索视图。此外，我们引入两个度量——算法稳定性与算法相似性，分别用于量化单个算法多次运行间的搜索轨迹一致性，以及不同算法间的相似程度。该方法应用于十种数值元启发式算法，揭示了它们在稳定性与对比行为上的深层特征，深化了对搜索动态的理解。
原文摘要 · Abstract (English)
Understanding the behavior of numerical metaheuristic optimization algorithms is critical for advancing their development and application. Traditional visualization techniques, such as convergence plots, trajectory mapping, and fitness landscape analysis, often fall short in illustrating the structural dynamics of the search process, especially in high-dimensional or complex solution spaces. To address this, we propose a novel representation and visualization methodology that clusters solution candidates explored by the algorithm and tracks the evolution of cluster memberships across iterations, offering a dynamic and interpretable view of the search process. Additionally, we introduce two metrics - algorithm stability and algorithm similarity- to quantify the consistency of search trajectories across runs of an individual algorithm and the similarity between different algorithms, respectively. We apply this methodology to a set of ten numerical metaheuristic algorithms, revealing insights into their stability and comparative behaviors, thereby providing a deeper understanding of their search dynamics.
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