Enhancing robustness of the inverted PBI scalarizing method in MOEA/D
•Search behavior of the inverted penalty-based boundary intersection (IPBI) scalarizing function in the decomposition based multi-objective evolutionary algorithm (MOEA/D) has been investigated.•Shortcomings of the IPBI scalarizing function have been discussed. In addition, both experimental and the...
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| Published in: | Applied soft computing Vol. 71; pp. 1117 - 1132 |
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| Format: | Journal Article |
| Language: | English |
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01.10.2018
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | •Search behavior of the inverted penalty-based boundary intersection (IPBI) scalarizing function in the decomposition based multi-objective evolutionary algorithm (MOEA/D) has been investigated.•Shortcomings of the IPBI scalarizing function have been discussed. In addition, both experimental and theoretical analysis have been conducted to explain the reasons causing these shortcomings.•Two improvement strategies are proposed to enhance the robustness of the IPBI scalarizing function in MOEA/D.
The scalarizing function design is an important issue influencing significantly the performance of a decomposition based multi-objective optimization algorithm (MOEA/D). Recently, an inverted penalty-based boundary intersection (IPBI) scalarizing function was proposed to improve the spread of solutions obtained by MOEA/D. Despite its effectiveness, MOEA/D with IPBI scalarizing function (MOEA/D-IPBI) still has several shortcomings: MOEA/D-IPBI often fails to obtain any solution within certain Pareto front (PF) regions. Furthermore, it may produce and retain unwanted dominated solutions outside the PF for some problems. In this work, we first analyze the reasons for the above two shortcomings of the IPBI scalarizing function, and then propose two improvement strategies, i.e., the adaptive reference point setting strategy and the adaptive subproblem replacement strategy, to overcome the two shortcomings of the IPBI scalarizing function respectively, giving rise to an enhanced MOEA/D with robust IPBI scalarizing method (R-IPBI). Experimental studies on WFG benchmark problems and the real-world reservoir flood control operation problems suggest that the two improvement strategies are very effective in overcoming the two shortcomings of the IPBI scalarizing function. As a result, the proposed R-IPBI algorithm is shown to be able to outperform the original MOEA/D-IPBI reliably. |
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| AbstractList | •Search behavior of the inverted penalty-based boundary intersection (IPBI) scalarizing function in the decomposition based multi-objective evolutionary algorithm (MOEA/D) has been investigated.•Shortcomings of the IPBI scalarizing function have been discussed. In addition, both experimental and theoretical analysis have been conducted to explain the reasons causing these shortcomings.•Two improvement strategies are proposed to enhance the robustness of the IPBI scalarizing function in MOEA/D.
The scalarizing function design is an important issue influencing significantly the performance of a decomposition based multi-objective optimization algorithm (MOEA/D). Recently, an inverted penalty-based boundary intersection (IPBI) scalarizing function was proposed to improve the spread of solutions obtained by MOEA/D. Despite its effectiveness, MOEA/D with IPBI scalarizing function (MOEA/D-IPBI) still has several shortcomings: MOEA/D-IPBI often fails to obtain any solution within certain Pareto front (PF) regions. Furthermore, it may produce and retain unwanted dominated solutions outside the PF for some problems. In this work, we first analyze the reasons for the above two shortcomings of the IPBI scalarizing function, and then propose two improvement strategies, i.e., the adaptive reference point setting strategy and the adaptive subproblem replacement strategy, to overcome the two shortcomings of the IPBI scalarizing function respectively, giving rise to an enhanced MOEA/D with robust IPBI scalarizing method (R-IPBI). Experimental studies on WFG benchmark problems and the real-world reservoir flood control operation problems suggest that the two improvement strategies are very effective in overcoming the two shortcomings of the IPBI scalarizing function. As a result, the proposed R-IPBI algorithm is shown to be able to outperform the original MOEA/D-IPBI reliably. |
| Author | Li, Xiaodong Miao, Qiguang Yu, Jusheng Quan, Yining Qi, Yutao |
| Author_xml | – sequence: 1 givenname: Yutao surname: Qi fullname: Qi, Yutao organization: School of Computer Science and Technology, Xidian University, Xi’an, China – sequence: 2 givenname: Jusheng surname: Yu fullname: Yu, Jusheng organization: School of Computer Science and Technology, Xidian University, Xi’an, China – sequence: 3 givenname: Xiaodong surname: Li fullname: Li, Xiaodong organization: School of Science, RMIT University, Melbourne, VIC, Australia – sequence: 4 givenname: Yining surname: Quan fullname: Quan, Yining organization: School of Computer Science and Technology, Xidian University, Xi’an, China – sequence: 5 givenname: Qiguang orcidid: 0000-0002-2872-388X surname: Miao fullname: Miao, Qiguang organization: School of Computer Science and Technology, Xidian University, Xi’an, China |
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| Cites_doi | 10.1109/TEVC.2015.2457616 10.1109/TEVC.2016.2608507 10.1109/TEVC.2003.810758 10.1109/TEVC.2007.892759 10.1109/TEVC.2015.2443001 10.1007/s10732-015-9301-6 10.1162/106365601750190406 10.1109/4235.797969 10.1109/TCYB.2015.2403849 10.1137/S1052623496307510 10.1007/s00158-009-0460-7 10.1016/j.ins.2016.06.005 10.1109/TEVC.2005.861417 10.1109/CEC.2015.7257248 10.1162/EVCO_a_00109 |
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| Keywords | Scalarizing function Evolutionary multi-objective optimization Decomposition method Inverted penalty-based boundary intersection |
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