p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications
In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion...
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| Veröffentlicht in: | Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-25 |
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| creator | Ding, Man Song, Lina Chen, Hanning Liu, Rui |
| description | In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems. |
| doi_str_mv | 10.1155/2019/7561398 |
| format | Article |
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The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2019/7561398</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Engineering ; Generators ; Genetic algorithms ; Mathematical problems ; Multiple objective analysis ; Optimality criteria ; Optimization ; Population density ; Portfolio management ; Power flow</subject><ispartof>Mathematical problems in engineering, 2019, Vol.2019 (2019), p.1-25</ispartof><rights>Copyright © 2019 Rui Liu et al.</rights><rights>Copyright © 2019 Rui Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c317t-c6c7e44bfc122e1c947e8ed71a8c1a95454b52fbbb443d62f89268e41f99e0443</cites><orcidid>0000-0002-9885-6653</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,4010,27904,27905,27906</link.rule.ids></links><search><contributor>Perera, Ricardo</contributor><contributor>Ricardo Perera</contributor><creatorcontrib>Ding, Man</creatorcontrib><creatorcontrib>Song, Lina</creatorcontrib><creatorcontrib>Chen, Hanning</creatorcontrib><creatorcontrib>Liu, Rui</creatorcontrib><title>p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications</title><title>Mathematical problems in engineering</title><description>In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. 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The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems.</description><subject>Algorithms</subject><subject>Engineering</subject><subject>Generators</subject><subject>Genetic algorithms</subject><subject>Mathematical problems</subject><subject>Multiple objective analysis</subject><subject>Optimality criteria</subject><subject>Optimization</subject><subject>Population density</subject><subject>Portfolio management</subject><subject>Power flow</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0M1LwzAYBvAgCs7pzbMUPGpd3jRpk-McOgVl4Ad4K2mauIxuqUnm2H9vRweCF08JD7834X0QOgd8A8DYiGAQo4LlkAl-gAbA8ixlQIvD7o4JTYFkH8foJIQFxgQY8AEq23TWRruUjY3b9FYGXSfP6yZaVy20ivZbJy_OxeR1G6JeJlPvNnGejJtP522cL0NinP87MG7bxirZRatwio6MbII-259D9H5_9zZ5SJ9m08fJ-ClVGRQxVbkqNKWVUUCIBiVoobmuC5BcgRSMMloxYqqqojSrc2K4IDnXFIwQGnfZEF3277befa11iOXCrf2q-7IkRLCMMsFJp657pbwLwWtTtr7b3W9LwOWuwnJXYbmvsONXPZ_bVS039j990WvdGW3krwZRYM6yH_OCe6c</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Ding, Man</creator><creator>Song, Lina</creator><creator>Chen, Hanning</creator><creator>Liu, Rui</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-9885-6653</orcidid></search><sort><creationdate>2019</creationdate><title>p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications</title><author>Ding, Man ; Song, Lina ; Chen, Hanning ; Liu, Rui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-c6c7e44bfc122e1c947e8ed71a8c1a95454b52fbbb443d62f89268e41f99e0443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Engineering</topic><topic>Generators</topic><topic>Genetic algorithms</topic><topic>Mathematical problems</topic><topic>Multiple objective analysis</topic><topic>Optimality criteria</topic><topic>Optimization</topic><topic>Population density</topic><topic>Portfolio management</topic><topic>Power flow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ding, Man</creatorcontrib><creatorcontrib>Song, Lina</creatorcontrib><creatorcontrib>Chen, Hanning</creatorcontrib><creatorcontrib>Liu, Rui</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ding, Man</au><au>Song, Lina</au><au>Chen, Hanning</au><au>Liu, Rui</au><au>Perera, Ricardo</au><au>Ricardo Perera</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2019</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>25</epage><pages>1-25</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2019/7561398</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0002-9885-6653</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Engineering Generators Genetic algorithms Mathematical problems Multiple objective analysis Optimality criteria Optimization Population density Portfolio management Power flow |
| title | p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications |
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