An augmented Lagrangian ant colony based method for constrained optimization

被引：0
作者：
Asghar Mahdavi
Mohammad Ebrahim Shiri
机构：
[1] Amirkabir University of Technology,Department of Mathematics and Computer Science
来源：
Computational Optimization and Applications | 2015年 / 60卷
关键词：
Ant colony; Augmented Lagrangian function (ALF); Constrained optimization problems (COPs);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
One of the most efficient penalty based methods to solve constrained optimization problems is the augmented Lagrangian algorithm. This paper presents a constrained optimization algorithm to solve continuous constrained global optimization problems. The proposed algorithm integrates the benefit of the continuous ant colony (ACOR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {ACO}_\mathrm{R}$$\end{document}) capability for discovering the global optimum with the effective behavior of the Lagrangian multiplier method to handle constraints. This method is tested on 13 well-known benchmark functions and compared with four other state-of-the-art algorithms.
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页码：263 / 276
页数：13
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