An improved clustering-based multi-swarm PSO using local diversification and topology information

TL;DR

This paper introduces an enhanced multi-swarm PSO algorithm that uses local search and concavity-based clustering to better detect multiple peaks, improving performance on benchmark niching problems.

Contribution

The paper proposes two novel enhancements—local search and concavity analysis—for clustering-based multi-swarm PSO, significantly improving peak detection capabilities.

Findings

Achieved higher peak detection ratios on IEEE CEC2013 datasets.

Outperformed three competing multi-swarm PSO algorithms in tests.

Enhanced ability to identify multiple optima in complex landscapes.

Abstract

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.