MOEA/D with Random Partial Update Strategy

Yuri Lavinas; Claus Aranha; Marcelo Ladeira; Felipe Campelo

doi:10.1109/CEC48606.2020.9185527

MOEA/D with Random Partial Update Strategy

Yuri Lavinas, Claus Aranha, Marcelo Ladeira, Felipe Campelo

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	978-1-7281-6929-3
ISBN (Print)	978-1-7281-6930-9
DOIs	https://doi.org/10.1109/CEC48606.2020.9185527
Publication status	Published - 3 Sept 2020
Event	2020 IEEE Congress on Evolutionary Computation - Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 https://ieeexplore.ieee.org/xpl/conhome/9178820/proceeding

Conference

Conference	2020 IEEE Congress on Evolutionary Computation
Abbreviated title	CEC 2020
Country/Territory	United Kingdom
City	Glasgow
Period	19/07/20 → 24/07/20
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9178820/proceeding

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

Access to Document

10.1109/CEC48606.2020.9185527

MOEA/D with Random Partial Update Strategy
© 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.
Accepted author manuscript, 2.03 MB

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title = "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.",

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AU - Aranha, Claus

AU - Ladeira, Marcelo

AU - Campelo, Felipe

N1 - © 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.

PY - 2020/9/3

Y1 - 2020/9/3

N2 - 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.

AB - 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.

KW - MOEA/D

KW - Multi-Objective Optimization

KW - Partial Update Strategy

KW - Resource Allocation

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M3 - Conference publication

SN - 978-1-7281-6930-9

BT - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

PB - IEEE

T2 - 2020 IEEE Congress on Evolutionary Computation

Y2 - 19 July 2020 through 24 July 2020

ER -

MOEA/D with Random Partial Update Strategy

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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