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

  • Vesela VasilevaEmail author
  • Kalin Penev
Part of the Studies in Computational Intelligence book series (SCI, volume 657)


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.


Global optimisation Multidimensional optimisation Numerical tests Free search Particle swarm optimisation Heuristic methods 



Preparation of this article is supported by Southampton Solent University Research & Enterprise Fund, grant 516/17062011.

I would like to thank also to my students Asim Al Nashwan, Dimitrios Kalfas, Georgius Haritonidis and Michael Borg for the design, implementation and overclocking of desktop PC used for completion of the experiments presented in this article.


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© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Technology School Maritime and Technology FacultySouthampton Solent UniversitySouthamptonUK

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