eprintid: 2327
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/23/27
datestamp: 2023-11-09 15:50:32
lastmod: 2023-11-09 15:50:32
status_changed: 2023-11-09 15:42:30
type: article
metadata_visibility: show
creators_name: Vasant, P.
title: Hybrid mesh adaptive direct search and genetic algorithms techniques for industrial production systems
ispublished: pub
note: cited By 28
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
publisher: Institute of Automatic Control - Silesian University of Technology
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-81755169865&doi=10.2478%2fv10170-010-0045-0&partnerID=40&md5=2a2260078c2f5cde2d990ddabe925140
id_number: 10.2478/v10170-010-0045-0
full_text_status: none
publication: Archives of Control Sciences
volume: 21
number: 3
pagerange: 299-312
refereed: TRUE
issn: 12302384
citation:   Vasant, P.  (2011) Hybrid mesh adaptive direct search and genetic algorithms techniques for industrial production systems.  Archives of Control Sciences, 21 (3).  pp. 299-312.  ISSN 12302384