MOEA/D with Random Partial Update Strategy

Yuri Lavinas, Claus Aranha, Marcelo Ladeira, Felipe Campelo

Research output: Chapter in Book/Published conference outputConference publication


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
Number of pages8
ISBN (Electronic)978-1-7281-6929-3
ISBN (Print)978-1-7281-6930-9
Publication statusPublished - 3 Sept 2020
Event2020 IEEE Congress on Evolutionary Computation - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020


Conference2020 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2020
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note

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


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