MOEA/D with Random Partial Update Strategy

Abstract

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.

Publication DOI: https://doi.org/10.1109/CEC48606.2020.9185527
Divisions: College of Engineering & Physical Sciences
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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
Full Text Link:
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)

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