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 fr...

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Published in:arXiv.org 2020-01
Main Authors: Lavinas, Yuri, Aranha, Claus, Ladeira, Marcelo, Campelo, Felipe
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Language:English
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Algorithms
Performance enhancement
Resource allocation
Strategy
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Aranha, Claus
Ladeira, Marcelo
Campelo, Felipe
description 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, simpler partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D with relative improvement-based resource allocation. The results indicate that using the 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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subjects Algorithms
Performance enhancement
Resource allocation
Strategy
title MOEA/D with Random Partial Update Strategy
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