提升多群智能算法找多个最优解的能力，避免遗漏局部峰值。
An improved clustering-based multi-swarm PSO using local diversification and topology information
- 先对初始粒子进行局部搜索，确保区域充分探测
- 通过凹度分析识别单个簇内的多个潜在最优区
- 在标准测试集上比现有方法更少遗漏峰值，适合复杂优化问题
多群粒子优化算法因能同时定位多个最优解而受到关注。其中基于聚类的多群算法通过将相近粒子聚成独立子群，探索潜在优势区域。但多数方法依赖欧氏距离，仅检测簇内一个峰值，易因分辨率不足丢失多个峰值。为此，本文提出两项改进：一是对初始粒子进行预局部搜索，确保各局部区域充分探测；二是采用基于凹度分析的探究式聚类，评估单个簇内多个子区域的潜力。由此提出的改进型聚类多群粒子群优化（TImPSO）在IEEE CEC2013多峰测试集上，与同类算法对比，在几乎所有测试函数中均获得更高的峰值发现率。
原文摘要 · Abstract (English)
Multi-swarm particle optimisation algorithms are gaining popularity due to their ability to locate multiple optimum points concurrently. In this family of algorithms, clustering-based multi-swarm algorithms are among the most effective techniques that join the closest particles together to form independent niche swarms that exploit potential promising regions. However, most clustering-based multi-swarms are Euclidean distance-based and only inquire about the potential of one peak within a cluster and thus can lose multiple peaks due to poor resolution. In a bid to improve the peak detection ratio, the current study proposes two enhancements. First, a preliminary local search across initial particles is proposed to ensure that each local region is sufficiently scouted prior to particle collaboration. Secondly, an investigative clustering approach that performs concavity analysis is proposed to evaluate the potential for several sub-niches within a single cluster. An improved clustering-based multi-swarm PSO (TImPSO) has resulted from these enhancements and has been tested against three competing algorithms in the same family using the IEEE CEC2013 niching datasets, resulting in an improved peak ratio for almost all the test functions.
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