Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3938

Title: Dynamic multi-objective optimisation using PSO
Authors: Greeff, M
Engelbrecht, AP
Keywords: Dynamic multi-objective optimisation
Pareto optimal front
Vector evaluated particle swarm optimiser
Dynamic multi-objective optimisation problem
Particle swarm optimisation
Issue Date: 2010
Publisher: Springer. Part of Springer Science+Business Media
Citation: Greeff, M and Engelbrecht, AP. 2009. Dynamic multi-objective optimisation using PSO. Studies in Computational Intelligence, (Series Ed: Kacprzyk, Janusz), pp 105-123
Abstract: Optimisation problems occur in many situations and aspects of modern life. In reality, many of these problems are dynamic in nature, where changes can occur in the environment that influences the solutions of the optimisation problem. Many methods use a weighted average approach to the multiple objectives. However, generally a dynamic multi-objective optimisation problem (DMOOP) does not have a single solution. In many cases the objectives (or goals) are in conflict with one another, where an improvement in one objective leads to a worse solution for at least one of the other objectives. The set of solutions that can be found where no other solution is better for all the objectives is called the Pareto optimal front (POF) and the solutions are called non-dominated solutions. The goal when solving a DMOOP is not to find a single solution, but to find the POF. This chapter introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve DMOOPs. Every objective is solved by one swarm and the swarms share knowledge amongst each other about the objective that it is solving. Not much work has been done on using this approach in dynamic environments. This chapter discusses this approach, as well as the effect that various ways of transferring knowledge between the swarms, together with the population size and various response methods to a detected change, have on the performance of the algorithm.
Description: Copyright: 2010 Springer. Part of Springer Science+Business Media. Permission to archive this author version is granted by Springer. Part of Springer Science+Business Media
URI: http://www.springer.com/series/7092
ISSN: 1860-949X
Appears in Collections:Digital intelligence
Mobile intelligent autonomous systems
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Greeff_2010.pdf221.12 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback