A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem
This research extends the sub-population genetic algorithm and combines it with a global archive and an adaptive strategy to solve the multi-objective parallel scheduling problems. In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identifi...
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| creator | Chang, Pei-Chann Chen, Shih-Hsin Hsieh, Jih-Chang |
| description | This research extends the sub-population genetic algorithm and combines it with a global archive and an adaptive strategy to solve the multi-objective parallel scheduling problems. In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identified, other subpopulations are able to employ this Pareto solution to further guide the searching direction. In addition, the crossover and mutation rates are continuously adapted according to the performance of the current generation. As a result, the convergence and diversity of the evolutionary processes can be maintained in a very efficient manner. Intensive experimental results indicate that the sub-population genetic algorithm combing the global archive and the adaptive strategy outperforms NSGA II and SPEA II approaches. |
| doi_str_mv | 10.1007/11881070_98 |
| format | Conference Proceeding |
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In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identified, other subpopulations are able to employ this Pareto solution to further guide the searching direction. In addition, the crossover and mutation rates are continuously adapted according to the performance of the current generation. As a result, the convergence and diversity of the evolutionary processes can be maintained in a very efficient manner. Intensive experimental results indicate that the sub-population genetic algorithm combing the global archive and the adaptive strategy outperforms NSGA II and SPEA II approaches.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540459019</identifier><identifier>ISBN: 3540459014</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540459026</identifier><identifier>EISBN: 3540459022</identifier><identifier>DOI: 10.1007/11881070_98</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptive Strategy ; Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Computer science; control theory; systems ; Exact sciences and technology ; Genetic Algorithm ; Multiobjective Optimization ; Parallel Machine ; Schedule Problem ; Theoretical computing</subject><ispartof>Advances in Natural Computation, 2006, p.730-739</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2008 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11881070_98$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11881070_98$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4050,4051,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19970793$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Wu, Feng</contributor><contributor>Liu, Jing</contributor><contributor>Wang, Lipo</contributor><contributor>Gao, Xin-bo</contributor><contributor>Jiao, Licheng</contributor><creatorcontrib>Chang, Pei-Chann</creatorcontrib><creatorcontrib>Chen, Shih-Hsin</creatorcontrib><creatorcontrib>Hsieh, Jih-Chang</creatorcontrib><title>A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem</title><title>Advances in Natural Computation</title><description>This research extends the sub-population genetic algorithm and combines it with a global archive and an adaptive strategy to solve the multi-objective parallel scheduling problems. In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identified, other subpopulations are able to employ this Pareto solution to further guide the searching direction. In addition, the crossover and mutation rates are continuously adapted according to the performance of the current generation. As a result, the convergence and diversity of the evolutionary processes can be maintained in a very efficient manner. Intensive experimental results indicate that the sub-population genetic algorithm combing the global archive and the adaptive strategy outperforms NSGA II and SPEA II approaches.</description><subject>Adaptive Strategy</subject><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Genetic Algorithm</subject><subject>Multiobjective Optimization</subject><subject>Parallel Machine</subject><subject>Schedule Problem</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540459019</isbn><isbn>3540459014</isbn><isbn>9783540459026</isbn><isbn>3540459022</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpVkT1PwzAQhs2XRFU68Qe8MDAEfLHzcWNUQUEqohIwR3bipC5uEjkJqBN_HdMywA13w_PolU4vIZfAboCx5BYgTYElLMf0iMwwSXkkmIiQhfExmUAMEHAu8OQfAzwlE8ZZGGAi-DmZ9f2G-eEQszCZkK-MLmyrpKWZK9bmQ9OXUQWrthutHEzb0IVu9GAKmtm6dWZYb-mn3zQrZTfs9cHJQdc7ahr6NNrBBK3a6GLPVtJJa7UNnqTPbrxcrHU5WtPUdOVaZfX2gpxV0vZ69nun5O3-7nX-ECyfF4_zbBl0IeAQpHEpNWKoKtBlmCpdlGEsEmACkAsuYs79u6pUiFWpIuURRH5EVGJYoORTcnXI7WRfSFs52RSmzztnttLtckBMWILce9cHr_eoqbXLVdu-9zmw_KeE_E8J_BveSHRh</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Chang, Pei-Chann</creator><creator>Chen, Shih-Hsin</creator><creator>Hsieh, Jih-Chang</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem</title><author>Chang, Pei-Chann ; Chen, Shih-Hsin ; Hsieh, Jih-Chang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-86dae992bf1ed28becd2647104193434633045bdb99fdb5b4711555545d92c9a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Adaptive Strategy</topic><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Genetic Algorithm</topic><topic>Multiobjective Optimization</topic><topic>Parallel Machine</topic><topic>Schedule Problem</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Pei-Chann</creatorcontrib><creatorcontrib>Chen, Shih-Hsin</creatorcontrib><creatorcontrib>Hsieh, Jih-Chang</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Pei-Chann</au><au>Chen, Shih-Hsin</au><au>Hsieh, Jih-Chang</au><au>Wu, Feng</au><au>Liu, Jing</au><au>Wang, Lipo</au><au>Gao, Xin-bo</au><au>Jiao, Licheng</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem</atitle><btitle>Advances in Natural Computation</btitle><date>2006</date><risdate>2006</risdate><spage>730</spage><epage>739</epage><pages>730-739</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540459019</isbn><isbn>3540459014</isbn><eisbn>9783540459026</eisbn><eisbn>3540459022</eisbn><abstract>This research extends the sub-population genetic algorithm and combines it with a global archive and an adaptive strategy to solve the multi-objective parallel scheduling problems. In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identified, other subpopulations are able to employ this Pareto solution to further guide the searching direction. In addition, the crossover and mutation rates are continuously adapted according to the performance of the current generation. As a result, the convergence and diversity of the evolutionary processes can be maintained in a very efficient manner. Intensive experimental results indicate that the sub-population genetic algorithm combing the global archive and the adaptive strategy outperforms NSGA II and SPEA II approaches.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11881070_98</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Natural Computation, 2006, p.730-739 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19970793 |
| source | Springer Books |
| subjects | Adaptive Strategy Algorithmics. Computability. Computer arithmetics Applied sciences Computer science control theory systems Exact sciences and technology Genetic Algorithm Multiobjective Optimization Parallel Machine Schedule Problem Theoretical computing |
| title | A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem |
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