ABSTRACT

Most optimisation problems in everyday life are not static in nature, 
have multiple objectives and at least two of the objectives are in 
conflict with one another. However, most research focusses on either 
static multi-objective optimisation (MOO) or dynamic singleobjective 
optimisation (DSOO). Furthermore, most research on dynamic multi-objective 
optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only 
a few particle swarm optimisation (PSO) algorithms exist. This thesis 
proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle 
Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation 
problems (DMOOPs). In order to determine whether an algorithm solves 
DMOO efficiently, functions are required that resembles real world 
DMOOPs, called benchmark functions, as well as functions that quantify 
the performance of the algorithm, called performance measures. However, 
one major problem in the field of DMOO is a lack of standard benchmark 
functions and performance measures. To address this problem, an overview 
is provided from the current literature and shortcomings of current DMOO 
benchmark functions and performance measures are discussed. In addition, 
new DMOOPs are introduced to address the identified shortcomings of current 
benchmark functions. Guides guide the optimisation process of DVEPSO. 
Therefore, various guide update approaches are investigated. Furthermore, 
a sensitivity analysis of DVEPSO is conducted to determine the influence 
of various parameters on the performance of DVEPSO. The investigated 
parameters include approaches to manage boundary constraint violations, 
approaches to share knowledge between the sub-swarms and responses to 
changes in the environment that are applied to either the particles of 
the sub-swarms or the non-dominated solutions stored in the archive. From 
these experiments the best DVEPSO configuration is determined and compared 
against four state-of-the-art DMOO algorithms.