The Research Based on the Discrete Cellular Ant Algorithm in the Geometric Constraint Solving

CAO Chun-hong; WANG Li-min; ZHAO Da-zhe

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The Research Based on the Discrete Cellular Ant Algorithm in the Geometric Constraint Solving

更新时间：2025-07-16

- The Research Based on the Discrete Cellular Ant Algorithm in the Geometric Constraint Solving
- Acta Electronica Sinica Vol. 39, Issue 5, Pages: 1127-1130(2011)
- 作者机构：
  
  1. 东北大学信息科学与工程学院,辽宁,沈阳,110819
  2. 吉林大学计算机科学与技术学院,吉林,长春,130012
  3. 计算机软件新技术国家重点实验室,南京大学,江苏,南京,210093
  4. 医学影像计算教育部重点实验室,东北大学,辽宁,沈阳,110819
  5. 东北大学信息科学与工程学院辽宁沈阳,110819
  6. 吉林大学计算机科学与技术学院吉林长春,130012
  7. 计算机软件新技术国家重点实验室南京大学江苏南京,210093
  8. 医学影像计算教育部重点实验室东北大学辽宁沈阳,110819
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.72
- Published：2011
- 稿件说明：
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CAO Chun-hong, WANG Li-min, ZHAO Da-zhe. The Research Based on the Discrete Cellular Ant Algorithm in the Geometric Constraint Solving[J]. Acta Electronica Sinica, 2011, 39(5): 1127-1130.
DOI：

CAO Chun-hong, WANG Li-min, ZHAO Da-zhe. The Research Based on the Discrete Cellular Ant Algorithm in the Geometric Constraint Solving[J]. Acta Electronica Sinica, 2011, 39(5): 1127-1130. DOI：

摘要

一个约束描述了一个应该被满足的关系

一旦用户已经定义了一系列的关系

那么在修改参数之后

系统会自动选择合适的状态来满足约束.在将几何约束问题的约束方程组转化为优化模型的时候

引入一种利用元胞演化规律和蚂蚁寻优特点的离散元胞蚂蚁算法.离散元胞蚂蚁算法是一种新型的仿生算法

它利用元胞在离散元胞空间的演化规律和蚂蚁寻优的特点

为解决实际问题提供了一种优化方法.实验表明

该方法可以比较有效的处理几何约束问题.

Abstract

A constraint can describe a relation to be satisfied.Once the user defines a series of relations

the system will select a proper state to satisfy the constraints after the parameters are modified.When transferring the geometric constraint equation group into the optimization model

we adopt a discrete cellular ant algorithm (DCAA) by evolutionary rule of cells and characteristics of ant colony optimization.Discrete cellular ant algorithm is a new type of bionic algorithm

which uses the evolution law of cellular in the discrete cellular space and the characteristics of ant optimization

and it provides an optimal way for solving practical problems.The experiment shows that the algorithm can solve the geometric constraint problems efficiently.

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