Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization

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Two_Arch2 : An Improved Two-Archive Algorithm for Many-Objective Optimization. / Wang, Handing; Jiao, Licheng; Yao, Xin.

In: IEEE Transactions on Evolutionary Computation, Vol. 19, No. 4, 6883177, 01.08.2015, p. 524-541.

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@article{7fe8660333104b52be9c7c675a278b3d,
title = "Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization",
abstract = "Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two-Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two-Arch, we propose a significantly improved two-archive algorithm (i.e., Two-Arch2) for ManyOPs in this paper. In our Two-Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new Lp-norm-based ( p",
keywords = "Evolutionary algorithm, Lp-norm, manyobjective optimization, two-archive algorithm (Two-Arch)",
author = "Handing Wang and Licheng Jiao and Xin Yao",
year = "2015",
month = aug,
day = "1",
doi = "10.1109/TEVC.2014.2350987",
language = "English",
volume = "19",
pages = "524--541",
journal = "IEEE Transactions on Evolutionary Computation",
issn = "1089-778X",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "4",

}

RIS

TY - JOUR

T1 - Two_Arch2

T2 - An Improved Two-Archive Algorithm for Many-Objective Optimization

AU - Wang, Handing

AU - Jiao, Licheng

AU - Yao, Xin

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two-Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two-Arch, we propose a significantly improved two-archive algorithm (i.e., Two-Arch2) for ManyOPs in this paper. In our Two-Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new Lp-norm-based ( p

AB - Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two-Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two-Arch, we propose a significantly improved two-archive algorithm (i.e., Two-Arch2) for ManyOPs in this paper. In our Two-Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new Lp-norm-based ( p

KW - Evolutionary algorithm

KW - Lp-norm

KW - manyobjective optimization

KW - two-archive algorithm (Two-Arch)

UR - http://www.scopus.com/inward/record.url?scp=84938564711&partnerID=8YFLogxK

U2 - 10.1109/TEVC.2014.2350987

DO - 10.1109/TEVC.2014.2350987

M3 - Article

AN - SCOPUS:84938564711

VL - 19

SP - 524

EP - 541

JO - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

SN - 1089-778X

IS - 4

M1 - 6883177

ER -