Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests from Two to Hundred Dimensions

Vesela Vasileva, Kalin Penev

Research output: Chapter in Book/Report/Published conference proceedingChapter

Abstract

This article presents an investigation on two real-value methods such as Free Search (FS) and Particle Swarm Optimisation (PSO) applied to global optimisation numerical tests. The objective is to identify how to facilitate assessment of heuristic, evolutionary, adaptive and other optimisation and search algorithms. Particular aim is to assess: (1) probability for success of given method; (2) abilities of given method for entire search space coverage; (3) dependence on initialisation; (4) abilities of given method to escape from trapping in local sub-optima; (5) abilities of explored methods to resolve multidimensional (one hundred dimensions) global optimisation tasks; (6) performance on two and hundred dimensional tasks; (7) minimal number of objective function calculation for resolving hundred dimensional tasks with acceptable level of precision. Achieved experimental results are presented and analysed. Discussion on FS and PSO essential characteristics concludes the article.
Original languageEnglish
Title of host publicationRecent Contributions in Intelligent Systems
EditorsVassil Sgurev, Ronald Yager, Janusz Kacprzyk, Krassimir Atanassov
Place of PublicationSwitzerland
PublisherSpringer International Publishing AG
Chapter17
Pages313-337
Number of pages24
Volume657
Edition1
ISBN (Electronic)978-3-319-41438-6
ISBN (Print)978-3-319-41437-9, 978-3-319-82354-6
DOIs
Publication statusPublished - 2017

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Nature Switzerland AG
ISSN (Print)1860-949X

Fingerprint

Global optimization
Global Optimization
Particle swarm optimization (PSO)
Particle Swarm Optimization
Trapping
Initialization
Search Space
One Dimension
Search Algorithm
Resolve
Optimization Algorithm
Coverage
Objective function
Entire
Heuristics
Experimental Results

Cite this

Vasileva, V., & Penev, K. (2017). Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests from Two to Hundred Dimensions. In V. Sgurev, R. Yager, J. Kacprzyk, & K. Atanassov (Eds.), Recent Contributions in Intelligent Systems (1 ed., Vol. 657, pp. 313-337). (Studies in Computational Intelligence). Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-41438-6
Vasileva, Vesela ; Penev, Kalin. / Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests from Two to Hundred Dimensions. Recent Contributions in Intelligent Systems. editor / Vassil Sgurev ; Ronald Yager ; Janusz Kacprzyk ; Krassimir Atanassov. Vol. 657 1. ed. Switzerland : Springer International Publishing AG, 2017. pp. 313-337 (Studies in Computational Intelligence).
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Vasileva, V & Penev, K 2017, Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests from Two to Hundred Dimensions. in V Sgurev, R Yager, J Kacprzyk & K Atanassov (eds), Recent Contributions in Intelligent Systems. 1 edn, vol. 657, Studies in Computational Intelligence, Springer International Publishing AG, Switzerland, pp. 313-337. https://doi.org/10.1007/978-3-319-41438-6

Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests from Two to Hundred Dimensions. / Vasileva, Vesela ; Penev, Kalin.

Recent Contributions in Intelligent Systems. ed. / Vassil Sgurev; Ronald Yager; Janusz Kacprzyk; Krassimir Atanassov. Vol. 657 1. ed. Switzerland : Springer International Publishing AG, 2017. p. 313-337 (Studies in Computational Intelligence).

Research output: Chapter in Book/Report/Published conference proceedingChapter

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Vasileva V, Penev K. Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests from Two to Hundred Dimensions. In Sgurev V, Yager R, Kacprzyk J, Atanassov K, editors, Recent Contributions in Intelligent Systems. 1 ed. Vol. 657. Switzerland: Springer International Publishing AG. 2017. p. 313-337. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-319-41438-6