A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem

Purpose – This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO. Design/metho...

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Main Author:	M. F. F., Ab Rashid
Format:	Article
Language:	English
Published:	Emerald Publishing Limited 2017
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/17664/ http://umpir.ump.edu.my/id/eprint/17664/ http://umpir.ump.edu.my/id/eprint/17664/ http://umpir.ump.edu.my/id/eprint/17664/1/fkm-2017-fadzil-A%20hybrid%20Ant-Wolf%20Algorithm1.pdf

id	ump-17664
recordtype	eprints
spelling	ump-176642017-05-12T07:06:20Z http://umpir.ump.edu.my/id/eprint/17664/ A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem M. F. F., Ab Rashid TS Manufactures Purpose – This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO. Design/methodology/approach – The ASP problem is formulated by using task-based representation. The HAWA adopts a global pheromone-updating procedure using the leadership hierarchy concept from the GWO into the ACO to enhance the algorithm performance. In GWO, three leaders are assigned to guide the search direction, instead of a single leader in most of the metaheuristic algorithms. Three assembly case studies used to test the algorithm performance. Findings – The proposed HAWA performed better in comparison to the Genetic Algorithm, ACO and GWO because of the balance between exploration and exploitation. The best solution guides the search direction, while the neighboring solutions from leadership hierarchy concept avoid the algorithm trapped in a local optimum. Originality/value – The originality of this research is on the proposed HAWA. In addition to the standard pheromone-updating procedure, a global pheromone-updating procedure is introduced, which adopted leadership hierarchy concept from GWO. Emerald Publishing Limited 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17664/1/fkm-2017-fadzil-A%20hybrid%20Ant-Wolf%20Algorithm1.pdf M. F. F., Ab Rashid (2017) A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem. Assembly Automation, 37 (2). pp. 238-248. ISSN 0144-5154 http://dx.doi.org/10.1108/AA-11-2016-143 doi: 10.1108/AA-11-2016-143
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures M. F. F., Ab Rashid A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
description	Purpose – This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO. Design/methodology/approach – The ASP problem is formulated by using task-based representation. The HAWA adopts a global pheromone-updating procedure using the leadership hierarchy concept from the GWO into the ACO to enhance the algorithm performance. In GWO, three leaders are assigned to guide the search direction, instead of a single leader in most of the metaheuristic algorithms. Three assembly case studies used to test the algorithm performance. Findings – The proposed HAWA performed better in comparison to the Genetic Algorithm, ACO and GWO because of the balance between exploration and exploitation. The best solution guides the search direction, while the neighboring solutions from leadership hierarchy concept avoid the algorithm trapped in a local optimum. Originality/value – The originality of this research is on the proposed HAWA. In addition to the standard pheromone-updating procedure, a global pheromone-updating procedure is introduced, which adopted leadership hierarchy concept from GWO.
format	Article
author	M. F. F., Ab Rashid
author_facet	M. F. F., Ab Rashid
author_sort	M. F. F., Ab Rashid
title	A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
title_short	A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
title_full	A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
title_fullStr	A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
title_full_unstemmed	A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem
title_sort	hybrid ant-wolf algorithm to optimize assembly sequence planning problem
publisher	Emerald Publishing Limited
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/17664/ http://umpir.ump.edu.my/id/eprint/17664/ http://umpir.ump.edu.my/id/eprint/17664/ http://umpir.ump.edu.my/id/eprint/17664/1/fkm-2017-fadzil-A%20hybrid%20Ant-Wolf%20Algorithm1.pdf
first_indexed	2023-09-18T22:24:32Z
last_indexed	2023-09-18T22:24:32Z
_version_	1777415878338412544

A Hybrid Ant-Wolf Algorithm to Optimize Assembly Sequence Planning Problem

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