Particle Swarm Optimization: Development of a General-Purpose Optimizer

Mauro Innocente, Johann Sienz

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

For problems where the quality of any solution can be quantified in a numerical value, optimization is the process of finding the permitted combination of variables in the problem that optimizes that value. Traditional methods present a very restrictive range of applications, mainly limited by the features of the function to be optimized and of the constraint functions. In contrast, evolutionary algorithms present almost no restriction to the features of these functions, although the most appropriate constraint-handling technique is still an open question. The particle swarm optimization (PSO) method is sometimes viewed as another evolutionary algorithm because of their many similarities, despite not being inspired by the same metaphor. Namely, they evolve a population of
individuals taking into consideration previous experiences and using stochastic operators to introduce new responses. The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature for decades. While all such advantages are valid when comparing the PSO paradigm to traditional methods, its main advantages with respect to evolutionary algorithms consist of its noticeably lower computational cost and easier implementation. In fact, the plain version can be programmed
in a few lines of code, involving no operator design and few parameters to be tuned. This paper deals with three important aspects of the method: the influence of the parameters’ tuning on the behaviour of the system; the design of stopping criteria so that the reliability of the solution found can be somehow estimated and computational cost can be saved; and the development of appropriate techniques to handle constraints, given that the original method is designed for unconstrained optimization problems.
Original languageEnglish
Title of host publicationProceedings of the 6th ASMO UK Conference on Engineering Design Optimization
EditorsJohann Sienz, Osvaldo M. Querin, Vassili V. Toropov, Peter D. Gosling
PublisherAssociation for Structural and Multidisciplinary Optimization in the UK
Pages203-223
Number of pages21
ISBN (Electronic)0-85316-251-4
Publication statusPublished - Jul 2006
Externally publishedYes
Event6th ASMO UK Conference on Engineering Design Optimization - St Edmund Hall, Oxford, United Kingdom
Duration: 3 Jul 20064 Jul 2006
http://www.asmo-uk.com/6th-asmo-uk/html/menu_page.html

Conference

Conference6th ASMO UK Conference on Engineering Design Optimization
CountryUnited Kingdom
CityOxford
Period3/07/064/07/06
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Cite this

Innocente, M., & Sienz, J. (2006). Particle Swarm Optimization: Development of a General-Purpose Optimizer. In J. Sienz, O. M. Querin, V. V. Toropov, & P. D. Gosling (Eds.), Proceedings of the 6th ASMO UK Conference on Engineering Design Optimization (pp. 203-223). Association for Structural and Multidisciplinary Optimization in the UK.