MOEA/D with Random Partial Update Strategy


Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work, we investigate a new, more straightforward partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D-DE using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D-DE with relative improvement-based resource allocation. The results indicate that using MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced.

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Divisions: College of Engineering & Physical Sciences
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Event Title: 2020 IEEE Congress on Evolutionary Computation
Event Type: Other
Event Dates: 2020-07-19 - 2020-07-24
Uncontrolled Keywords: MOEA/D,Multi-Objective Optimization,Partial Update Strategy,Resource Allocation,Control and Optimization,Decision Sciences (miscellaneous),Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture
ISBN: 978-1-7281-6930-9, 978-1-7281-6929-3
Last Modified: 17 Jun 2024 08:34
Date Deposited: 30 Sep 2020 11:18
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Related URLs: https://ieeexpl ... ocument/9185527 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2020-09-03
Accepted Date: 2020-07-01
Authors: Lavinas, Yuri
Aranha, Claus
Ladeira, Marcelo
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)



Version: Accepted Version

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