An adaptation reference-point-based multiobjective evolutionary algorithm

Juan ZOU; Liuwei FU; Shengxiang YANG; Jinhua ZHENG; Gan RUAN; Tingrui PEI; Lei WANG

doi:10.1016/j.ins.2019.03.020

An adaptation reference-point-based multiobjective evolutionary algorithm

Juan ZOU, Liuwei FU^*, Shengxiang YANG, Jinhua ZHENG, Gan RUAN, Tingrui PEI, Lei WANG

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

18 Citations (Scopus)

Abstract

It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics.

Original language	English
Pages (from-to)	41-57
Number of pages	17
Journal	Information Sciences
Volume	488
Early online date	11 Mar 2019
DOIs	https://doi.org/10.1016/j.ins.2019.03.020
Publication status	Published - Jul 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2019

Funding

The authors wish to thank the support of the National Natural Science Foundation of China (Grant No. 61876164, 61673331, 61772178), the Education Department Major Project of Hunan Province (Grant No.17A212), the Science and Technology Plan Project of Hunan Province (Grant No.2018TP1036, 2016TP1020), the Provinces and Cities Joint Foundation Project (Grant No.2017JJ4001).

Keywords

Evolutionary algorithms
Genetic algorithms
Many-objective optimization
Multiobjective optimization

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10.1016/j.ins.2019.03.020

Cite this

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title = "An adaptation reference-point-based multiobjective evolutionary algorithm",

abstract = "It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics.",

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AU - ZOU, Juan

AU - FU, Liuwei

AU - YANG, Shengxiang

AU - ZHENG, Jinhua

AU - RUAN, Gan

AU - PEI, Tingrui

AU - WANG, Lei

PY - 2019/7

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N2 - It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics.

AB - It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics.

KW - Evolutionary algorithms

KW - Genetic algorithms

KW - Many-objective optimization

KW - Multiobjective optimization

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DO - 10.1016/j.ins.2019.03.020

M3 - Journal Article (refereed)

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SN - 0020-0255

VL - 488

SP - 41

EP - 57

JO - Information Sciences

JF - Information Sciences

ER -

An adaptation reference-point-based multiobjective evolutionary algorithm

Abstract

Bibliographical note

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Keywords

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