Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints

Wanfeng Shang; Shengdun Zhao; Yajing Shen; Liangliang Shi

Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints

Wanfeng Shang, Shengdun Zhao, Yajing Shen, Liangliang Shi

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

Abstract

A hybrid method combining a genetic algorithm with a flexible tolerance algorithm is proposed for global optimization problems with multiple nonlinear constraints and peaks. The adaptive genetic algorithm is used to localize the 'best' areas, while the flexible tolerance algorithm exploits this area by search mechanism for quasi-feasible point. To evaluate the efficiency of this method, a complex function with six peaks and four constraints is implemented and compared with the results supplied by sequential uniconstrained minimization technique (SUMT), which indicates that the hybrid method is able to improve convergence and reduce computing task greatly.

Original language	English
Pages (from-to)	1267-1270
Journal	Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University
Volume	41
Issue number	11
Publication status	Published - Nov 2007
Externally published	Yes

Research Keywords

Adaptive genetic algorithm
Flexible tolerance algorithm
Multiconstraint
Optimization

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title = "Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints",

abstract = "A hybrid method combining a genetic algorithm with a flexible tolerance algorithm is proposed for global optimization problems with multiple nonlinear constraints and peaks. The adaptive genetic algorithm is used to localize the 'best' areas, while the flexible tolerance algorithm exploits this area by search mechanism for quasi-feasible point. To evaluate the efficiency of this method, a complex function with six peaks and four constraints is implemented and compared with the results supplied by sequential uniconstrained minimization technique (SUMT), which indicates that the hybrid method is able to improve convergence and reduce computing task greatly.",

keywords = "Adaptive genetic algorithm, Flexible tolerance algorithm, Multiconstraint, Optimization",

author = "Wanfeng Shang and Shengdun Zhao and Yajing Shen and Liangliang Shi",

year = "2007",

month = nov,

language = "English",

volume = "41",

pages = "1267--1270",

journal = "Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University",

issn = "0253-987X",

publisher = "Xi'an Jiaotong University",

number = "11",

}

Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints. / Shang, Wanfeng; Zhao, Shengdun; Shen, Yajing et al.
In: Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, Vol. 41, No. 11, 11.2007, p. 1267-1270.

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

TY - JOUR

T1 - Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints

AU - Shang, Wanfeng

AU - Zhao, Shengdun

AU - Shen, Yajing

AU - Shi, Liangliang

PY - 2007/11

Y1 - 2007/11

N2 - A hybrid method combining a genetic algorithm with a flexible tolerance algorithm is proposed for global optimization problems with multiple nonlinear constraints and peaks. The adaptive genetic algorithm is used to localize the 'best' areas, while the flexible tolerance algorithm exploits this area by search mechanism for quasi-feasible point. To evaluate the efficiency of this method, a complex function with six peaks and four constraints is implemented and compared with the results supplied by sequential uniconstrained minimization technique (SUMT), which indicates that the hybrid method is able to improve convergence and reduce computing task greatly.

AB - A hybrid method combining a genetic algorithm with a flexible tolerance algorithm is proposed for global optimization problems with multiple nonlinear constraints and peaks. The adaptive genetic algorithm is used to localize the 'best' areas, while the flexible tolerance algorithm exploits this area by search mechanism for quasi-feasible point. To evaluate the efficiency of this method, a complex function with six peaks and four constraints is implemented and compared with the results supplied by sequential uniconstrained minimization technique (SUMT), which indicates that the hybrid method is able to improve convergence and reduce computing task greatly.

KW - Adaptive genetic algorithm

KW - Flexible tolerance algorithm

KW - Multiconstraint

KW - Optimization

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M3 - RGC 22 - Publication in policy or professional journal

SN - 0253-987X

VL - 41

SP - 1267

EP - 1270

JO - Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University

JF - Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University

IS - 11

ER -

Genetic algorithm and flexible tolerance algorithm hybridized for global optimization problems with multiple constraints

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