A two-phase evolutionary algorithm framework for multi-objective optimization

This paper proposes a two-phase evolutionary algorithm framework for solving multi-objective optimization problems (MOPs), which allows different users to flexibly handle MOPs with different existing algorithms. In the first phase, a specific multi-objective evolutionary algorithm (MOEA) with a smal...

Full description

Saved in:
Bibliographic Details
Main Authors: Jiang, S., Chen, Zefeng
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/10356/154500
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-154500
record_format dspace
spelling sg-ntu-dr.10356-1545002021-12-23T08:02:42Z A two-phase evolutionary algorithm framework for multi-objective optimization Jiang, S. Chen, Zefeng School of Computer Science and Engineering Engineering::Computer science and engineering Multi-Objective Optimization Evolutionary Algorithm This paper proposes a two-phase evolutionary algorithm framework for solving multi-objective optimization problems (MOPs), which allows different users to flexibly handle MOPs with different existing algorithms. In the first phase, a specific multi-objective evolutionary algorithm (MOEA) with a smaller population size is adopted to fast obtain a population converging to the true Pareto front. Then, in the second phase, a simple environmental selection mechanism based on a measure function and a well-designed crowdedness function is used to promote the uniformity of population in the objective space. Based on the proposed framework, we form four instantiations by embedding four distinct MOEAs into the first phase of the proposed framework. In the experimental study, different experiments are conducted on a variety of well-known benchmark problems from 3 to 10 objectives, and experimental results demonstrate the effect of the proposed framework. Furthermore, compared with several state-of-the-art multi-objective evolutionary algorithms, the four instantiations of the proposed framework have better performance and can obtain well-distributed solution sets. In short, the proposed framework has the strong ability to promote the performance of existing algorithms. This work was supported by the National Natural Science Foundation of China under Grant 61773410. 2021-12-23T08:02:42Z 2021-12-23T08:02:42Z 2021 Journal Article Jiang, S. & Chen, Z. (2021). A two-phase evolutionary algorithm framework for multi-objective optimization. Applied Intelligence, 51, 3952-3974. https://dx.doi.org/10.1007/s10489-020-01988-7 0924-669X https://hdl.handle.net/10356/154500 10.1007/s10489-020-01988-7 2-s2.0-85096592376 51 3952 3974 en Applied Intelligence © 2020 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Multi-Objective Optimization
Evolutionary Algorithm
spellingShingle Engineering::Computer science and engineering
Multi-Objective Optimization
Evolutionary Algorithm
Jiang, S.
Chen, Zefeng
A two-phase evolutionary algorithm framework for multi-objective optimization
description This paper proposes a two-phase evolutionary algorithm framework for solving multi-objective optimization problems (MOPs), which allows different users to flexibly handle MOPs with different existing algorithms. In the first phase, a specific multi-objective evolutionary algorithm (MOEA) with a smaller population size is adopted to fast obtain a population converging to the true Pareto front. Then, in the second phase, a simple environmental selection mechanism based on a measure function and a well-designed crowdedness function is used to promote the uniformity of population in the objective space. Based on the proposed framework, we form four instantiations by embedding four distinct MOEAs into the first phase of the proposed framework. In the experimental study, different experiments are conducted on a variety of well-known benchmark problems from 3 to 10 objectives, and experimental results demonstrate the effect of the proposed framework. Furthermore, compared with several state-of-the-art multi-objective evolutionary algorithms, the four instantiations of the proposed framework have better performance and can obtain well-distributed solution sets. In short, the proposed framework has the strong ability to promote the performance of existing algorithms.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Jiang, S.
Chen, Zefeng
format Article
author Jiang, S.
Chen, Zefeng
author_sort Jiang, S.
title A two-phase evolutionary algorithm framework for multi-objective optimization
title_short A two-phase evolutionary algorithm framework for multi-objective optimization
title_full A two-phase evolutionary algorithm framework for multi-objective optimization
title_fullStr A two-phase evolutionary algorithm framework for multi-objective optimization
title_full_unstemmed A two-phase evolutionary algorithm framework for multi-objective optimization
title_sort two-phase evolutionary algorithm framework for multi-objective optimization
publishDate 2021
url https://hdl.handle.net/10356/154500
_version_ 1720447107867869184