Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems
Task sequence planning (TSP) is the key factor to the efficiency, stableness, and cost of a complex assembly system. To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obt...
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| Veröffentlicht in: | Electronics letters 2018-07, Vol.54 (14), p.870-872 |
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| container_end_page | 872 |
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| container_issue | 14 |
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| container_title | Electronics letters |
| container_volume | 54 |
| creator | Zhang, Linbin Lv, Huanpei Tan, Dapeng Xu, Fang Chen, Jiaoliao Bao, Guanjun Cai, Shibo |
| description | Task sequence planning (TSP) is the key factor to the efficiency, stableness, and cost of a complex assembly system. To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obtain the optimal TSP scheme. The simulation results indicate that the proposed algorithm can perform higher efficiency and stableness than the previously reported methods. |
| doi_str_mv | 10.1049/el.2018.0609 |
| format | Article |
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To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obtain the optimal TSP scheme. The simulation results indicate that the proposed algorithm can perform higher efficiency and stableness than the previously reported methods.</description><identifier>ISSN: 0013-5194</identifier><identifier>ISSN: 1350-911X</identifier><identifier>EISSN: 1350-911X</identifier><identifier>DOI: 10.1049/el.2018.0609</identifier><language>eng</language><publisher>The Institution of Engineering and Technology</publisher><subject>adaptive quantum genetic algorithm ; assembling ; complex assembly system ; Control engineering ; genetic algorithms ; optimal TSP scheme ; planning ; task sequence planning</subject><ispartof>Electronics letters, 2018-07, Vol.54 (14), p.870-872</ispartof><rights>The Institution of Engineering and Technology</rights><rights>2020 The Institution of Engineering and Technology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3817-98b6f9c2a5936f88c737e9a5767d094e6fcc612aa7e74de17d9dd5dcf2b15853</citedby><cites>FETCH-LOGICAL-c3817-98b6f9c2a5936f88c737e9a5767d094e6fcc612aa7e74de17d9dd5dcf2b15853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1049%2Fel.2018.0609$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1049%2Fel.2018.0609$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids><linktorsrc>$$Uhttps://onlinelibrary.wiley.com/doi/abs/10.1049%2Fel.2018.0609$$EView_record_in_Wiley-Blackwell$$FView_record_in_$$GWiley-Blackwell</linktorsrc></links><search><creatorcontrib>Zhang, Linbin</creatorcontrib><creatorcontrib>Lv, Huanpei</creatorcontrib><creatorcontrib>Tan, Dapeng</creatorcontrib><creatorcontrib>Xu, Fang</creatorcontrib><creatorcontrib>Chen, Jiaoliao</creatorcontrib><creatorcontrib>Bao, Guanjun</creatorcontrib><creatorcontrib>Cai, Shibo</creatorcontrib><title>Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems</title><title>Electronics letters</title><description>Task sequence planning (TSP) is the key factor to the efficiency, stableness, and cost of a complex assembly system. To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obtain the optimal TSP scheme. The simulation results indicate that the proposed algorithm can perform higher efficiency and stableness than the previously reported methods.</description><subject>adaptive quantum genetic algorithm</subject><subject>assembling</subject><subject>complex assembly system</subject><subject>Control engineering</subject><subject>genetic algorithms</subject><subject>optimal TSP scheme</subject><subject>planning</subject><subject>task sequence planning</subject><issn>0013-5194</issn><issn>1350-911X</issn><issn>1350-911X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp90D1PwzAQBmALgUQF3fgBHhgYSLGTOI7HUrWAVImlA0yWY5-LwflonAD596QqA0PFdMvznu5ehK4omVGSijvws5jQfEYyIk7QhCaMRILSl1M0IYQmEaMiPUfTEFxBaErTjKR0gl7nRjWd-wS861XV9SXeQgWd01j5bd267q3Etm5xp8IHDrDrodKAG6-qylVbXFus67Lx8I1VCFAWfsBhCB2U4RKdWeUDTH_nBdqslpvFY7R-fnhazNeRTnLKI5EXmRU6Vkwkmc1zzRMOQjGecUNECpnVOqOxUhx4aoByI4xhRtu4oCxnyQW6PazVbR1CC1Y2rStVO0hK5L4YCV7ui5H7YkbODvzLeRj-tXK5Xsf3K8J4zsfc9SHnoJPvdd9W40-j-MMbY0d2c4QdveQHOnyAnA</recordid><startdate>20180712</startdate><enddate>20180712</enddate><creator>Zhang, Linbin</creator><creator>Lv, Huanpei</creator><creator>Tan, Dapeng</creator><creator>Xu, Fang</creator><creator>Chen, Jiaoliao</creator><creator>Bao, Guanjun</creator><creator>Cai, Shibo</creator><general>The Institution of Engineering and Technology</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20180712</creationdate><title>Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems</title><author>Zhang, Linbin ; Lv, Huanpei ; Tan, Dapeng ; Xu, Fang ; Chen, Jiaoliao ; Bao, Guanjun ; Cai, Shibo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3817-98b6f9c2a5936f88c737e9a5767d094e6fcc612aa7e74de17d9dd5dcf2b15853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>adaptive quantum genetic algorithm</topic><topic>assembling</topic><topic>complex assembly system</topic><topic>Control engineering</topic><topic>genetic algorithms</topic><topic>optimal TSP scheme</topic><topic>planning</topic><topic>task sequence planning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Linbin</creatorcontrib><creatorcontrib>Lv, Huanpei</creatorcontrib><creatorcontrib>Tan, Dapeng</creatorcontrib><creatorcontrib>Xu, Fang</creatorcontrib><creatorcontrib>Chen, Jiaoliao</creatorcontrib><creatorcontrib>Bao, Guanjun</creatorcontrib><creatorcontrib>Cai, Shibo</creatorcontrib><collection>CrossRef</collection><jtitle>Electronics letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Linbin</au><au>Lv, Huanpei</au><au>Tan, Dapeng</au><au>Xu, Fang</au><au>Chen, Jiaoliao</au><au>Bao, Guanjun</au><au>Cai, Shibo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems</atitle><jtitle>Electronics letters</jtitle><date>2018-07-12</date><risdate>2018</risdate><volume>54</volume><issue>14</issue><spage>870</spage><epage>872</epage><pages>870-872</pages><issn>0013-5194</issn><issn>1350-911X</issn><eissn>1350-911X</eissn><abstract>Task sequence planning (TSP) is the key factor to the efficiency, stableness, and cost of a complex assembly system. To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obtain the optimal TSP scheme. The simulation results indicate that the proposed algorithm can perform higher efficiency and stableness than the previously reported methods.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/el.2018.0609</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | Wiley Online Library Open Access |
| subjects | adaptive quantum genetic algorithm assembling complex assembly system Control engineering genetic algorithms optimal TSP scheme planning task sequence planning |
| title | Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems |
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