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primary topic: Hybrid mesh adaptive direct search and genetic algorithms techniques for industrial production systems

Hybrid mesh adaptive direct search and genetic algorithms techniques for industrial production systems
type: AcademicArticle
type: Article
abstract: In this paper, computational and simulation results are presented for the performance of the fitness function, decision variables and CPU time of the proposed hybridization method of Mesh Adaptive Direct Search (MADS) and Genetic Algorithm (GA). MADS is a class of direct search of algorithms for nonlinear optimization. The MADS algorithm is a modification of the Pattern Search (PS) algorithm. The algorithms differ in how the set of points forming the mesh is computed. The PS algorithm uses fixed direction vectors, whereas the MADS algorithm uses random selection of vectors to define the mesh. A key advantage of MADS over PS is that local exploration of the space of variables is not restricted to a finite number of directions (poll directions). This is the primary drawback of PS algorithms, and therefore the main motivation in using MADS to solve the industrial production planning problem is to overcome this restriction. A thorough investigation on hybrid MADS and GA is performed for the quality of the best fitness function, decision variables and computational CPU time.
date: 2011
volume: 21
issue: 3
is part of: Archives of Control Sciences
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Institute of Automatic Control - Silesian University of Technology
type: Organization

Archives of Control Sciences
type: Collection
issn: 12302384

P. Vasant
type: Person
given name: P.
family name: Vasant

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primary topic: Hybrid mesh adaptive direct search and genetic algorithms techniques for industrial production systems