Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 54-57.doi: 10.11896/JsJkx.191000179

• Artificial Intelligence • Previous Articles     Next Articles

Parametric-free Filled Function Algorithm for Unconstrained Optimization

ZHANG Yu-qin, ZHANG Jian-liang and FENG Xiang-dong   

  1. The Engineering & Technical College of Chengdu University of Technology,Leshan,Sichuan 614000,China
  • Published:2020-07-07
  • About author:ZHANG Yu-qin, born in 1977, master, lecture.Her main research interests include optimization theory and algorithm.
  • Supported by:
    This work was supported by the Key Natural Science ProJections of Sichuan Provincial Department of Education (18ZA0075,18ZA0073) and Fund ProJects of The Engineering & Technical College of Chengdu University of Technology (C122017043,C122017042).

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Abstract: Filled function method is a kind of important method for solving unconstrained optimization problem.The key of the method is to construct a filled function with good properties,simple form and easy to solve the minimum value.Based on the definition of filled function and certain conditions of the obJective function for unconstrained global optimization problem,a non-parameter filled function is proposed for solving this problem,which is simple and easy to be calculated.For the filled function,firstly,under suitable assumptions,some properties of filled function are studied and proved.Secondly,according to the related pro-perties,an algorithm suitable for this filled function algorithm is established.The filled function algorithm consists of two phases:the minimization phase and the filling phase.The two phases alternate until the termination criterion is met.Finally,through classical examples,numerical experiments are carried out and compared with the results of other literatures.Experiments results show that not only the filled function is feasible and the algorithm is effective,but also the results are accurate and the number of iterations is less.

Key words: Example experiment, Filled function, Global minimum, Global optimization, Local minimum

CLC Number: 

  • O224
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