Free Search—a comparative analysis

Kalin Penev, Guy Littlefair

Research output: Contribution to journalArticle

Abstract

The article presents a novel population-based optimisation method, called Free Search (FS). Essential peculiarities of the new method are introduced. The aim of the study is to identify how robust is Free Search. Explored and compared are four different population-based optimisation methods, namely Genetic Algorithm (in real coded BLX-α modification), Particle Swarm Optimisation, Differential Evolution and Free Search. They are applied to five non-linear, heterogeneous, numerical, optimisation problems. The achieved results suggest that Free Search has stable robust behaviour on explored tests; FS can cope with heterogeneous optimisation problems; FS is applicable to unknown (black-box) real-world optimisation tasks.
Original languageEnglish
Pages (from-to)173-193
Number of pages20
JournalInformation Sciences
Volume172
Issue number1-2
DOIs
Publication statusPublished - 9 Jun 2005

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Comparative Analysis
Optimization Methods
Optimization Problem
Particle swarm optimization (PSO)
Numerical Optimization
Differential Evolution
Black Box
Genetic algorithms
Particle Swarm Optimization
Comparative analysis
Genetic Algorithm
Unknown
Optimization

Cite this

Penev, Kalin ; Littlefair, Guy. / Free Search—a comparative analysis. In: Information Sciences. 2005 ; Vol. 172, No. 1-2. pp. 173-193.
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Free Search—a comparative analysis. / Penev, Kalin; Littlefair, Guy.

In: Information Sciences, Vol. 172, No. 1-2, 09.06.2005, p. 173-193.

Research output: Contribution to journalArticle

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