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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Vydáno v:Applied soft computing Ročník 71; s. 1117 - 1132
Hlavní autoři: Qi, Yutao, Yu, Jusheng, Li, Xiaodong, Quan, Yining, Miao, Qiguang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.10.2018
Témata:
Decomposition method
Evolutionary multi-objective optimization
Inverted penalty-based boundary intersection
Scalarizing function
Scalarizing function
Evolutionary multi-objective optimization
Decomposition method
Inverted penalty-based boundary intersection
ISSN:1568-4946, 1872-9681
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Shrnutí:•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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2017.11.029