Function optimization based on incremental resolution

Bin Xin; Jie Chen; Zhi Hong Peng

Function optimization based on incremental resolution

Bin Xin^*, Jie Chen, Zhi Hong Peng

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

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

Abstract

Aiming at solving the problems of function optimization with interval constraints, a class of optimization techniques based on incremental resolution is proposed by combining with Monte Carlo uniform random sampling and chaos search respectively. The reasons of that these optimization techniques are more efficient than the corresponding common optimizers with invariant resolution are analyzed. Dealing with function optimization problems, they can achieve the compression of searching space and the location of optimal region rapidly. Simulation results validate their effectiveness.

Original language	English
Pages (from-to)	131-134
Number of pages	4
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	27
Issue number	SUPPL. 1
Publication status	Published - May 2007

Keywords

Function optimization
Incremental resolution
Random number generator

Cite this

Xin, B., Chen, J., & Peng, Z. H. (2007). Function optimization based on incremental resolution. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 27(SUPPL. 1), 131-134.

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abstract = "Aiming at solving the problems of function optimization with interval constraints, a class of optimization techniques based on incremental resolution is proposed by combining with Monte Carlo uniform random sampling and chaos search respectively. The reasons of that these optimization techniques are more efficient than the corresponding common optimizers with invariant resolution are analyzed. Dealing with function optimization problems, they can achieve the compression of searching space and the location of optimal region rapidly. Simulation results validate their effectiveness.",

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AU - Xin, Bin

AU - Chen, Jie

AU - Peng, Zhi Hong

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AB - Aiming at solving the problems of function optimization with interval constraints, a class of optimization techniques based on incremental resolution is proposed by combining with Monte Carlo uniform random sampling and chaos search respectively. The reasons of that these optimization techniques are more efficient than the corresponding common optimizers with invariant resolution are analyzed. Dealing with function optimization problems, they can achieve the compression of searching space and the location of optimal region rapidly. Simulation results validate their effectiveness.

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KW - Incremental resolution

KW - Random number generator

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Function optimization based on incremental resolution

Abstract

Keywords

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