Title
Study on minimum zone evaluation of flatness errors based on a hybrid chaos optimization algorithm
Abstract
In this paper, according to characteristics of flatness error evaluation, a hybrid evaluation method to evaluate the minimum zone error is provided. The evolutional optimum model and the calculation process are introduced in detail. The hybrid optimization algorithm is based upon chaos optimization algorithm (COA) and Powell search. Compared with conventional optimum methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very good. Moreover, the efficiency of COA is much higher than some stochastic algorithms such as simulated anneal algorithm and genetic algorithm (GA) when COA is used to a kind of continuous problems. The hybrid optimization algorithm can improve the efficiency of searching in the whole field by gradually shrinking the area of optimization variable. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and GA, indicate that the proposed method does provide better accuracy on flatness error evaluation, and it has fast convergent speed as well as using computer expediently and popularizing application easily.
Year
DOI
Venue
2009
10.1007/978-3-642-04070-2_22
ICIC (1)
Keywords
Field
DocType
simulated anneal algorithm,hybrid optimization algorithm,genetic algorithm,flatness error evaluation,hybrid chaos optimization algorithm,simplex search,stochastic algorithm,conventional optimum method,powell method,minimum zone evaluation,powell optimum method,chaos optimization algorithm,global optimization,least square,simulated annealing algorithm
Stochastic algorithms,Least squares,Flatness (systems theory),Mathematical optimization,Computer science,Meta-optimization,Algorithm,Simplex,Chaos optimization,Optimization algorithm,Genetic algorithm
Conference
Volume
ISSN
ISBN
5754
0302-9743
3-642-04069-1
Citations 
PageRank 
References 
0
0.34
5
Authors
1
Name
Order
Citations
PageRank
Ke Zhang176.11