Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management
Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evoluti...
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| Veröffentlicht in: | Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-22 |
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| creator | Liu, Rui Sun, Liling He, Maowei Chen, Hanning Hu, Yabao Shen, Hai |
| description | Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness. |
| doi_str_mv | 10.1155/2019/8418369 |
| format | Article |
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Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2019/8418369</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Computer simulation ; Cooperation ; Cybernetics ; Elitism ; Engineering ; Evolutionary algorithms ; Genetic algorithms ; International conferences ; Investment ; Management ; Multiple objective analysis ; Objectives ; Optimality criteria ; Pareto optimization ; Performance evaluation ; Portfolio management ; Researchers ; Robustness (mathematics) ; Spectrum allocation ; Swarm intelligence ; Theory</subject><ispartof>Mathematical problems in engineering, 2019, Vol.2019 (2019), p.1-22</ispartof><rights>Copyright © 2019 Yabao Hu et al.</rights><rights>Copyright © 2019 Yabao Hu 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. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-9ec121a4b39952e93d546c7be9b8039e65f6384dfebd1fae8a54136a5bdc9d563</citedby><cites>FETCH-LOGICAL-c360t-9ec121a4b39952e93d546c7be9b8039e65f6384dfebd1fae8a54136a5bdc9d563</cites><orcidid>0000-0002-9885-6653 ; 0000-0003-3380-4497 ; 0000-0002-3264-9619 ; 0000-0003-2426-8397 ; 0000-0001-7493-5464 ; 0000-0002-2365-2269</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><contributor>Perera, Ricardo</contributor><contributor>Ricardo Perera</contributor><creatorcontrib>Liu, Rui</creatorcontrib><creatorcontrib>Sun, Liling</creatorcontrib><creatorcontrib>He, Maowei</creatorcontrib><creatorcontrib>Chen, Hanning</creatorcontrib><creatorcontrib>Hu, Yabao</creatorcontrib><creatorcontrib>Shen, Hai</creatorcontrib><title>Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management</title><title>Mathematical problems in engineering</title><description>Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.</description><subject>Computer simulation</subject><subject>Cooperation</subject><subject>Cybernetics</subject><subject>Elitism</subject><subject>Engineering</subject><subject>Evolutionary algorithms</subject><subject>Genetic algorithms</subject><subject>International conferences</subject><subject>Investment</subject><subject>Management</subject><subject>Multiple objective analysis</subject><subject>Objectives</subject><subject>Optimality criteria</subject><subject>Pareto optimization</subject><subject>Performance evaluation</subject><subject>Portfolio management</subject><subject>Researchers</subject><subject>Robustness (mathematics)</subject><subject>Spectrum allocation</subject><subject>Swarm intelligence</subject><subject>Theory</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>eNqFkM1LxDAQxYsouK7ePEvBo9bNJE3aHHXxC1xWUMFbSduJZmmbmmZd1r_erl3w4MHTPIbfm-G9IDgGcgHA-YQSkJM0hpQJuROMgAsWcYiT3V4TGkdA2et-cNB1C0IocEhHQT1bVt5ETyvl6nDQ83yBhTefGM5bb2rzhS68Uh2WoW3CNvpZqsr4dTh1xqMzKtTW_TE_Wue1rYwNZ6pRb1hj4w-DPa2qDo-2cxy83Fw_T--ih_nt_fTyISqYID6SWAAFFedMSk5RspLHokhylHlKmETBtWBpXGrMS9AKU8VjYELxvCxk2aceB6fD3dbZjyV2PlvYpWv6lxmFhNPeDBvqfKAKZ7vOoc5a10dz6wxItik02xSabQvt8bMBfzdNqVbmP_pkoLFnUKtfGmQigLNvSAWAZg</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Liu, Rui</creator><creator>Sun, Liling</creator><creator>He, Maowei</creator><creator>Chen, Hanning</creator><creator>Hu, Yabao</creator><creator>Shen, Hai</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><orcidid>https://orcid.org/0000-0003-3380-4497</orcidid><orcidid>https://orcid.org/0000-0002-3264-9619</orcidid><orcidid>https://orcid.org/0000-0003-2426-8397</orcidid><orcidid>https://orcid.org/0000-0001-7493-5464</orcidid><orcidid>https://orcid.org/0000-0002-2365-2269</orcidid></search><sort><creationdate>2019</creationdate><title>Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management</title><author>Liu, Rui ; Sun, Liling ; He, Maowei ; Chen, Hanning ; Hu, Yabao ; Shen, Hai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-9ec121a4b39952e93d546c7be9b8039e65f6384dfebd1fae8a54136a5bdc9d563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer simulation</topic><topic>Cooperation</topic><topic>Cybernetics</topic><topic>Elitism</topic><topic>Engineering</topic><topic>Evolutionary algorithms</topic><topic>Genetic algorithms</topic><topic>International conferences</topic><topic>Investment</topic><topic>Management</topic><topic>Multiple objective analysis</topic><topic>Objectives</topic><topic>Optimality criteria</topic><topic>Pareto optimization</topic><topic>Performance evaluation</topic><topic>Portfolio management</topic><topic>Researchers</topic><topic>Robustness (mathematics)</topic><topic>Spectrum allocation</topic><topic>Swarm intelligence</topic><topic>Theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Rui</creatorcontrib><creatorcontrib>Sun, Liling</creatorcontrib><creatorcontrib>He, Maowei</creatorcontrib><creatorcontrib>Chen, Hanning</creatorcontrib><creatorcontrib>Hu, Yabao</creatorcontrib><creatorcontrib>Shen, Hai</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 Journals</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>Liu, Rui</au><au>Sun, Liling</au><au>He, Maowei</au><au>Chen, Hanning</au><au>Hu, Yabao</au><au>Shen, Hai</au><au>Perera, Ricardo</au><au>Ricardo Perera</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2019</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>22</epage><pages>1-22</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2019/8418369</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-9885-6653</orcidid><orcidid>https://orcid.org/0000-0003-3380-4497</orcidid><orcidid>https://orcid.org/0000-0002-3264-9619</orcidid><orcidid>https://orcid.org/0000-0003-2426-8397</orcidid><orcidid>https://orcid.org/0000-0001-7493-5464</orcidid><orcidid>https://orcid.org/0000-0002-2365-2269</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Computer simulation Cooperation Cybernetics Elitism Engineering Evolutionary algorithms Genetic algorithms International conferences Investment Management Multiple objective analysis Objectives Optimality criteria Pareto optimization Performance evaluation Portfolio management Researchers Robustness (mathematics) Spectrum allocation Swarm intelligence Theory |
| title | Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management |
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