Two strategies of adaptive cluster covering with descent and their comparison to other algorithms
type: Item
instance of: scholarly article
title: Two strategies of adaptive cluster covering with descent and their comparison to other algorithms
zbMATH Open document ID: 0922.90129
DOI: 10.1023/A:1008334632441
published in: Journal of Global Optimization
publication date: 1999-10-19T00:00:00Z
review text: The methodology of global optimization is enriched by two strategies of randomized search based on clustering approaches: the adaptive cluster covering (ACCO) and the Adaptive Cluster Covering with descent (ACD). These methods apply to the set of multi-extremum minimization problems and can be compared to techniques as Controlled Random Search (CRS), as Genetic Algorithms (GA) and as multistart methods such as Multis and M-simplex. The basic ideas of adaptive clustering covering is to subdivide the domain of search for the global optimum into smaller subdomains by way of clustering; then the objective functions are assessed in the subdomains and using these results the subdomains are shrinked. The adaptive aspect of the strategy refers to a possible shifting in subregion of search together with a shrinking of it and a change in density of covering depending on the previous assessments of the global minimizer. As by these different steps the true localization of the global optimum is not yet assured, an action of periodic re-randomization of the initial population is added. The adaptive clustering covering with descent uses in addition the simplex descent features to look for better points within each subdomain obtained by clustering. An extensive comparative work has been performed to look for the best algorithm with respect to criteria such as effectiveness, efficiency and reliability. The problems considered varied from 2 to 10 dimensions and presented typically 1 to 10 local optima. Globally the ACCO method appeared to perform best, being comparable to CRS but faster. The ACD algorithm proved to be efficient and effective for functions with higher dimensions but more accurate tuning of its parameters is needed to improve its reliability.
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 91C20
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 1278746
zbMATH Keywords: global optimization
zbMATH Keywords: randomized search
zbMATH Keywords: clustering
zbMATH Keywords: adaptive cluster covering
author: Dimitri Solomatine
describes a project that uses: Global Optimization Toolbox For Maple
describes a project that uses: Genocop
describes a project that uses: BRENT
MaRDI profile type: Publication
full work available at URL: a:1008334632441
OpenAlex ID: W1608030021
description: scientific article; zbMATH DE number 1278746