On benchmarking Stochastic Global Optimization Algorithms

E.M.T. Hendrix, A. Lancinskas

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which type of instances, our focus is here on the benchmarking of the behavior of algorithms by applying experiments on test instances. We argue that a good minimum performance benchmark is due to pure random search; i.e. algorithms should do better. We introduce the concept of the cumulative
distribution function of the record value as a measure with the benchmark of pure random search and the idea of algorithms being dominated by others. The concepts are illustrated using frequently used algorithms.
LanguageEnglish
Pages649-662
JournalInformatica
Volume26
Issue number4
DOIs
Publication statusPublished - 2015

Fingerprint

Stochastic Optimization
Benchmarking
Global optimization
Global Optimization
Optimization Algorithm
Random Search
Constrained Global Optimization
Benchmark
Record Values
Heuristic Optimization
Stochastic Algorithms
Evaluation Function
Function evaluation
Constrained optimization
Optimization Problem
Experiment
Experiments
Concepts

Cite this

Hendrix, E.M.T. ; Lancinskas, A. / On benchmarking Stochastic Global Optimization Algorithms. In: Informatica. 2015 ; Vol. 26, No. 4. pp. 649-662.
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On benchmarking Stochastic Global Optimization Algorithms. / Hendrix, E.M.T.; Lancinskas, A.

In: Informatica, Vol. 26, No. 4, 2015, p. 649-662.

Research output: Contribution to journalArticleAcademicpeer-review

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