An Improved Artificial Bee Colony Algorithm With Q -Learning for Solving Permutation Flow-Shop Scheduling Problems
A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with Q -learning, named QABC, for solving it with minimizing the maximum completion time (makesp...
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| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2023-05, Vol.53 (5), p.1-10 |
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| creator | Li, Hanxiao Gao, Kaizhou Duan, Pei-Yong Li, Jun-Qing Zhang, Le |
| description | A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with Q -learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz-Enscore-Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase. Q -learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems. |
| doi_str_mv | 10.1109/TSMC.2022.3219380 |
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This work proposes an improved artificial bee colony (ABC) algorithm with <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz-Enscore-Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase. <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.]]></description><identifier>ISSN: 2168-2216</identifier><identifier>EISSN: 2168-2232</identifier><identifier>DOI: 10.1109/TSMC.2022.3219380</identifier><identifier>CODEN: ITSMFE</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject><inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> Q</tex-math> </inline-formula>-learning ; Artificial bee colony (ABC) ; Artificial bee colony algorithm ; Completion time ; Energy consumption ; Job shop scheduling ; Machine learning ; makespan ; Mathematical models ; Metaheuristics ; permutation flow-shop scheduling ; Permutations ; Q-learning ; Search algorithms ; Search problems ; Swarm intelligence</subject><ispartof>IEEE transactions on systems, man, and cybernetics. 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Systems</title><addtitle>TSMC</addtitle><description><![CDATA[A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz-Enscore-Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase. <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.]]></description><subject><inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> Q</tex-math> </inline-formula>-learning</subject><subject>Artificial bee colony (ABC)</subject><subject>Artificial bee colony algorithm</subject><subject>Completion time</subject><subject>Energy consumption</subject><subject>Job shop scheduling</subject><subject>Machine learning</subject><subject>makespan</subject><subject>Mathematical models</subject><subject>Metaheuristics</subject><subject>permutation flow-shop scheduling</subject><subject>Permutations</subject><subject>Q-learning</subject><subject>Search algorithms</subject><subject>Search problems</subject><subject>Swarm intelligence</subject><issn>2168-2216</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQhoMoWGp_gHhZ8Jy6H81m9xiLH4WKSioeQ7KZbbdssnWTVPrvTWzpZWYOzzvDPEFwS_CUECwfVunbfEoxpVNGiWQCXwQjSrgIKWX08jwTfh1MmmaLMSZUcIb5KPBJjRbVzrs9lCjxrdFGmdyiRwA0d9bVB5TYtfOm3VTou6_oE4VLyH1t6jXSzqPU2f0wf4CvujZvjavRs3W_YbpxO5SqDZSd_Qe8KyxUzU1wpXPbwOTUx8HX89Nq_hou318W82QZKipZG-pClDhiFAihWMbxrJxhoRVXQlDFiyjWqtBAJKexIH1ECQ6ElZqDios4p2wc3B_39t_9dNC02dZ1vu5PZlRgLDkjfNZT5Egp75rGg8523lS5P2QEZ4PdbLCbDXazk90-c3fMGAA481JGDEcx-wOAgnXh</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Li, Hanxiao</creator><creator>Gao, Kaizhou</creator><creator>Duan, Pei-Yong</creator><creator>Li, Jun-Qing</creator><creator>Zhang, Le</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-4283-4049</orcidid><orcidid>https://orcid.org/0000-0003-4614-4424</orcidid><orcidid>https://orcid.org/0000-0002-9252-6928</orcidid><orcidid>https://orcid.org/0000-0001-8440-9601</orcidid></search><sort><creationdate>20230501</creationdate><title>An Improved Artificial Bee Colony Algorithm With Q -Learning for Solving Permutation Flow-Shop Scheduling Problems</title><author>Li, Hanxiao ; Gao, Kaizhou ; Duan, Pei-Yong ; Li, Jun-Qing ; Zhang, Le</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-fb8d0532e11209774d408fc6c882c6b57fcbfe1962781c29c86e13df6ec7b7a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic><inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> Q</tex-math> </inline-formula>-learning</topic><topic>Artificial bee colony (ABC)</topic><topic>Artificial bee colony algorithm</topic><topic>Completion time</topic><topic>Energy consumption</topic><topic>Job shop scheduling</topic><topic>Machine learning</topic><topic>makespan</topic><topic>Mathematical models</topic><topic>Metaheuristics</topic><topic>permutation flow-shop scheduling</topic><topic>Permutations</topic><topic>Q-learning</topic><topic>Search algorithms</topic><topic>Search problems</topic><topic>Swarm intelligence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Hanxiao</creatorcontrib><creatorcontrib>Gao, Kaizhou</creatorcontrib><creatorcontrib>Duan, Pei-Yong</creatorcontrib><creatorcontrib>Li, Jun-Qing</creatorcontrib><creatorcontrib>Zhang, Le</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on systems, man, and cybernetics. Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Hanxiao</au><au>Gao, Kaizhou</au><au>Duan, Pei-Yong</au><au>Li, Jun-Qing</au><au>Zhang, Le</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Improved Artificial Bee Colony Algorithm With Q -Learning for Solving Permutation Flow-Shop Scheduling Problems</atitle><jtitle>IEEE transactions on systems, man, and cybernetics. Systems</jtitle><stitle>TSMC</stitle><date>2023-05-01</date><risdate>2023</risdate><volume>53</volume><issue>5</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>2168-2216</issn><eissn>2168-2232</eissn><coden>ITSMFE</coden><abstract><![CDATA[A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz-Enscore-Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase. <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSMC.2022.3219380</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-4283-4049</orcidid><orcidid>https://orcid.org/0000-0003-4614-4424</orcidid><orcidid>https://orcid.org/0000-0002-9252-6928</orcidid><orcidid>https://orcid.org/0000-0001-8440-9601</orcidid></addata></record> |
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| subjects | <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> Q</tex-math> </inline-formula>-learning Artificial bee colony (ABC) Artificial bee colony algorithm Completion time Energy consumption Job shop scheduling Machine learning makespan Mathematical models Metaheuristics permutation flow-shop scheduling Permutations Q-learning Search algorithms Search problems Swarm intelligence |
| title | An Improved Artificial Bee Colony Algorithm With Q -Learning for Solving Permutation Flow-Shop Scheduling Problems |
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