Enhancing robustness of the inverted PBI scalarizing method in MOEA/D

Qi, Y, Yu, J, Li, X, Quan, Y and Miao, Q 2018, 'Enhancing robustness of the inverted PBI scalarizing method in MOEA/D', Applied Soft Computing Journal, vol. 71, pp. 1117-1132.


Document type: Journal Article
Collection: Journal Articles

Title Enhancing robustness of the inverted PBI scalarizing method in MOEA/D
Author(s) Qi, Y
Yu, J
Li, X
Quan, Y
Miao, Q
Year 2018
Journal name Applied Soft Computing Journal
Volume number 71
Start page 1117
End page 1132
Total pages 16
Publisher Elsevier
Abstract 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.
Subject Neural, Evolutionary and Fuzzy Computation
Keyword(s) Decomposition method
Evolutionary multi-objective optimization
Inverted penalty-based boundary intersection
Scalarizing function
DOI - identifier 10.1016/j.asoc.2017.11.029
Copyright notice © 2017 Elsevier B.V. All rights reserved.
ISSN 1568-4946
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 5 Abstract Views  -  Detailed Statistics
Created: Thu, 31 Jan 2019, 11:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us