MOEA/D with Random Partial Update Strategy

Yuri Lavinas, Claus Aranha, Marcelo Ladeira, Felipe Campelo

Research output: Chapter in Book/Published conference outputConference publication

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.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-7281-6929-3
ISBN (Print)978-1-7281-6930-9
DOIs
Publication statusPublished - 3 Sep 2020
Event2020 IEEE Congress on Evolutionary Computation - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020
https://ieeexplore.ieee.org/xpl/conhome/9178820/proceeding

Conference

Conference2020 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20
Internet address

Bibliographical note

© 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.

Keywords

  • MOEA/D
  • Multi-Objective Optimization
  • Partial Update Strategy
  • Resource Allocation

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