Analysis of the Niching Particle Swarm Optimization Algorithm

Abstract

Multimodal optimization (MMO) techniques have been researched and developed over the years to track multiple global optima concurrently. MMO algorithms extend traditional unimodal optimization algorithms by using search strategies built around forming niches for multiple possible solutions. NichePSO was one of the first approaches to utilize particle swarm optimization (PSO) for MMO problems, using several small subswarms of agents working concurrently to form niches within the search space. Despite its promising performance NichePSO does suffer from some problems, and very little research has been done to study and improve upon the algorithm over the years. A main goal of this thesis is to analyze the NichePSO algorithm, gaining insight into the strengths and weaknesses of the algorithm. Empirical analyses were performed to study the NichePSO’s ability to maintain niches within complex problem domains, as well as methods for improving the overall performance and effectiveness of the algorithm. Two variants of the NichePSO algorithm are proposed, and experimental results show that they both significantly improve the performance of the NichePSO algorithm across several benchmark functions.