Particle swarm optimisation in dynamically changing environments - an empirical study
Julien Georges Omer Louis Duhain · UpSpace Institutional Repository (University of Pretoria) · 2012
Real-world optimisation problems often are of a dynamic nature. Recently, much re-search has been done to apply particle swarm optimisation (PSO) to dynamic environ-ments (DE). However, these research efforts generally focused on optimising one variation of the PSO algorithm for one type of DE. The aim of this work is to develop a more comprehensive view of PSO for DEs. This thesis studies different schemes of character-ising and taxonomising DEs, performance measures used to quantify the performance of optimisation algorithms applied to DEs, various adaptations of PSO to apply PSO to DEs, and the effectiveness of these approaches on different DE types. The standard PSO algorithm has shown limitations when applied to DEs. To over-come these limitations, the standard PSO can be modified using personal best re-evaluation, change detection and response, diversity maintenance, or swarm sub-division and parallel tracking of optima. To investigate the strengths and weaknesses of these ap-proaches, a representative sample of algorithms, namely, the standard PSO, re-evaluating PSO, reinitialising PSO, atomic PSO (APSO), quantum swarm optimisation (QSO),