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/methodol...
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| Veröffentlicht in: | Assembly automation 2017-01, Vol.37 (2), p.238-248 |
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| container_title | Assembly automation |
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| creator | Ab Rashid, Mohd Fadzil Faisae |
| 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. |
| doi_str_mv | 10.1108/AA-11-2016-143 |
| format | Article |
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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.</description><identifier>ISSN: 0144-5154</identifier><identifier>ISSN: 2754-6969</identifier><identifier>EISSN: 1758-4078</identifier><identifier>EISSN: 2754-6977</identifier><identifier>DOI: 10.1108/AA-11-2016-143</identifier><language>eng</language><publisher>Bingley: Emerald Publishing Limited</publisher><subject>Algorithms ; Ant colony optimization ; Assembly ; Genetic algorithms ; Heuristic methods ; Leadership ; Optimization ; Product development ; Production planning ; Researchers</subject><ispartof>Assembly automation, 2017-01, Vol.37 (2), p.238-248</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-e743cddf98c062fe1bfda5a0be6ac37a35f8fdd7806621716a69356b74ed1f683</citedby><cites>FETCH-LOGICAL-c381t-e743cddf98c062fe1bfda5a0be6ac37a35f8fdd7806621716a69356b74ed1f683</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/AA-11-2016-143/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,967,11635,27924,27925,52689</link.rule.ids></links><search><creatorcontrib>Ab Rashid, Mohd Fadzil Faisae</creatorcontrib><title>A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem</title><title>Assembly automation</title><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.</description><subject>Algorithms</subject><subject>Ant colony optimization</subject><subject>Assembly</subject><subject>Genetic algorithms</subject><subject>Heuristic methods</subject><subject>Leadership</subject><subject>Optimization</subject><subject>Product development</subject><subject>Production 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Faisae</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-e743cddf98c062fe1bfda5a0be6ac37a35f8fdd7806621716a69356b74ed1f683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Ant colony optimization</topic><topic>Assembly</topic><topic>Genetic algorithms</topic><topic>Heuristic methods</topic><topic>Leadership</topic><topic>Optimization</topic><topic>Product development</topic><topic>Production planning</topic><topic>Researchers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ab Rashid, Mohd Fadzil Faisae</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & 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Collection</collection><jtitle>Assembly automation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ab Rashid, Mohd Fadzil Faisae</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem</atitle><jtitle>Assembly automation</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>37</volume><issue>2</issue><spage>238</spage><epage>248</epage><pages>238-248</pages><issn>0144-5154</issn><issn>2754-6969</issn><eissn>1758-4078</eissn><eissn>2754-6977</eissn><abstract>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.</abstract><cop>Bingley</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/AA-11-2016-143</doi><tpages>11</tpages></addata></record> |
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| ispartof | Assembly automation, 2017-01, Vol.37 (2), p.238-248 |
| issn | 0144-5154 2754-6969 1758-4078 2754-6977 |
| language | eng |
| recordid | cdi_proquest_journals_1891702010 |
| source | Emerald A-Z Current Journals |
| subjects | Algorithms Ant colony optimization Assembly Genetic algorithms Heuristic methods Leadership Optimization Product development Production planning Researchers |
| title | A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem |
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