Convergence analysis of shuffled frog leaping algorithm and its modified algorithm

Yingying Xiao; Xudong Chai; Bohu Li; Qiusheng Wang

Convergence analysis of shuffled frog leaping algorithm and its modified algorithm

Yingying Xiao^*
, Xudong Chai
, Bohu Li
, Qiusheng Wang

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University
Beijing Simulation Center

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Based on the analysis of the conditions of shuffled frog leaping (SFL) algorithm, a modified SFL (MSFL) was proposed to deal with the premature and slow convergence. In MSFL, convergence was analyzed based on the theory of geometrical sequence, and two premature judgments were defined, including the dispersion and the fitness variance, to adaptively adjust the coefficients, for balancing convergence accuracy and rate. Six benchmark functions were proposed to test the performances of MSFL, and experiment simulations show that MSFL has higher precision and good stability.

Original language	English
Pages (from-to)	15-18+28
Journal	Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)
Volume	40
Issue number	7
State	Published - Jul 2012

Keywords

Convergence analysis
Geometrical sequence
Multi-objective optimization problem
Shuffled frog leaping (SFL) algorithm
Update policies

Cite this

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title = "Convergence analysis of shuffled frog leaping algorithm and its modified algorithm",

abstract = "Based on the analysis of the conditions of shuffled frog leaping (SFL) algorithm, a modified SFL (MSFL) was proposed to deal with the premature and slow convergence. In MSFL, convergence was analyzed based on the theory of geometrical sequence, and two premature judgments were defined, including the dispersion and the fitness variance, to adaptively adjust the coefficients, for balancing convergence accuracy and rate. Six benchmark functions were proposed to test the performances of MSFL, and experiment simulations show that MSFL has higher precision and good stability.",

keywords = "Convergence analysis, Geometrical sequence, Multi-objective optimization problem, Shuffled frog leaping (SFL) algorithm, Update policies",

author = "Yingying Xiao and Xudong Chai and Bohu Li and Qiusheng Wang",

year = "2012",

month = jul,

language = "英语",

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pages = "15--18+28",

journal = "Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)",

issn = "1671-4512",

publisher = "Huazhong University of Science and Technology",

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Convergence analysis of shuffled frog leaping algorithm and its modified algorithm. / Xiao, Yingying; Chai, Xudong; Li, Bohu et al.
In: Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), Vol. 40, No. 7, 07.2012, p. 15-18+28.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Convergence analysis of shuffled frog leaping algorithm and its modified algorithm

AU - Xiao, Yingying

AU - Chai, Xudong

AU - Li, Bohu

AU - Wang, Qiusheng

PY - 2012/7

Y1 - 2012/7

N2 - Based on the analysis of the conditions of shuffled frog leaping (SFL) algorithm, a modified SFL (MSFL) was proposed to deal with the premature and slow convergence. In MSFL, convergence was analyzed based on the theory of geometrical sequence, and two premature judgments were defined, including the dispersion and the fitness variance, to adaptively adjust the coefficients, for balancing convergence accuracy and rate. Six benchmark functions were proposed to test the performances of MSFL, and experiment simulations show that MSFL has higher precision and good stability.

AB - Based on the analysis of the conditions of shuffled frog leaping (SFL) algorithm, a modified SFL (MSFL) was proposed to deal with the premature and slow convergence. In MSFL, convergence was analyzed based on the theory of geometrical sequence, and two premature judgments were defined, including the dispersion and the fitness variance, to adaptively adjust the coefficients, for balancing convergence accuracy and rate. Six benchmark functions were proposed to test the performances of MSFL, and experiment simulations show that MSFL has higher precision and good stability.

KW - Convergence analysis

KW - Geometrical sequence

KW - Multi-objective optimization problem

KW - Shuffled frog leaping (SFL) algorithm

KW - Update policies

UR - https://www.scopus.com/pages/publications/84865804831

M3 - 文章

AN - SCOPUS:84865804831

SN - 1671-4512

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SP - 15-18+28

JO - Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)

JF - Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)

IS - 7

ER -

Convergence analysis of shuffled frog leaping algorithm and its modified algorithm

Abstract

Keywords

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