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运筹学学报 >

2025 , Vol. 29 >Issue 2: 141 - 157

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2025.02.011

论文

一种新的全局优化无参数填充函数方法

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  • 1. 北方民族大学数学与信息科学学院, 宁夏银川 750021
    2. 金陵科技学院理学院, 江苏南京 211169
    3. 宁夏大学数学统计学院, 宁夏银川 750021
高岳林 E-mail: gaoyuelin@163.com

收稿日期: 2021-12-19

  网络出版日期: 2025-06-12

基金资助

国家自然科学基金(11961001);宁夏高等教育一流学科建设项目(NXYLXK2017B09);北方民族大学重大专项(ZDZX201901);北方民族大学研究生创新资助项目(YCX22096)

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运筹学学报编辑部, 2025, 版权所有，未经授权，不得转载。
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A new non parameter-filled function method for global optimization

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  • 1. School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, Ningxia, China
    2. College of Science, Jinling Institute of Technology, Nanjing 211169, Jiangsu, China
    3. School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, Ningxia, China

Received date: 2021-12-19

  Online published: 2025-06-12

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, 2025, All rights reserved. Unauthorized reproduction is prohibited.
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摘要

填充函数法是一种用于寻找无约束优化问题全局最优解的确定性方法, 这种方法的核心技术是构造填充函数, 使得迭代过程不断跳出当前的局部极小点。目前见到的填充函数一般都含有参数, 而参数的选取对算法的计算效果影响较大。本文利用填充函数的定义, 具体构造出一个新的无参数填充函数, 由此提出了新的全局优化无参数填充函数方法, 数值实验表明, 该方法是可行的和有效的, 具有更好的全局寻优能力。

关键词： 全局优化; 无约束优化问题; 填充函数方法; 无参数

本文引用格式

马素霞, 高岳林, 林洪伟, 张博 . 一种新的全局优化无参数填充函数方法[J]. 运筹学学报, 2025 , 29(2) : 141 -157 . DOI: 10.15960/j.cnki.issn.1007-6093.2025.02.011

Abstract

The filled function method is a kind of deterministic method, which is adopted to find the global optimal solution for the unconstrained optimization problem. The core technique of this method is to construct the filled function, which is such that the iterative process of the algorithm constantly jump out of the current local minimizer. Currently, the filled function generally contains parameters, and the selection of parameters has a great influence on the computation effect of the algorithm. In this paper, a new non parameter-filled function is constructed by using the definition of filled function, and a new global optimization method is developed. Numerical experiments illustrate that this method is feasible and effective, and has better global optimization ability.

Key words： global optimization; unconstrained optimization problem; filled function method; non parameter

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