A Chaos Genetic Algorithm Based Access Selection in Heterogeneous Wireless Networks

TANG Liang-rui; LI Wen-meng; SHENG Jie; FAN Bing

doi:10.3969/j.issn.0372-2112.2014.08.016

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A Chaos Genetic Algorithm Based Access Selection in Heterogeneous Wireless Networks

更新时间：2025-07-16

- A Chaos Genetic Algorithm Based Access Selection in Heterogeneous Wireless Networks
- Acta Electronica Sinica Vol. 42, Issue 8, Pages: 1564-1570(2014)
- 作者机构：
  
  1. 华北电力大学新能源电力系统国家重点实验室,北京,102206
  2. 苏州大学城市轨道交通学院,江苏,苏州,215021
  3. 华北电力大学新能源电力系统国家重点实验室,北京,102206
  4. 苏州大学城市轨道交通学院,江苏,苏州,215021
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2014.08.016
  CLC： TN929.5
- Published：2014
- 稿件说明：
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TANG Liang-rui, LI Wen-meng, SHENG Jie, et al. A Chaos Genetic Algorithm Based Access Selection in Heterogeneous Wireless Networks[J]. Acta Electronica Sinica, 2014, 42(8): 1564-1570.
DOI：

TANG Liang-rui, LI Wen-meng, SHENG Jie, et al. A Chaos Genetic Algorithm Based Access Selection in Heterogeneous Wireless Networks[J]. Acta Electronica Sinica, 2014, 42(8): 1564-1570. DOI： 10.3969/j.issn.0372-2112.2014.08.016.

摘要

为了解决异构无线网络接入选择问题，提出一种基于混沌遗传算法的解决方法.将网络接入选择转换为一个多属性优化问题，利用混沌遗传算法解决全局寻优问题.算法首先通过超混沌系统产生初始种群和混沌扰动向量，对遗传算法进行改进；然后利用混沌遗传算法对适应度函数求解得到网络选择评价指标权重.仿真结果表明，该算法能够使新到达的用户更均匀地分布在各备选网络中，有效降低网络阻塞率并减小各候选网络阻塞率差距，实现网络的负载均衡.

Abstract

In order to solve the issue of access selection in heterogeneous wireless networks

a new algorithm based on chaos genetic algorithm is proposed

of which the issue is converted into a multiple attribute optimization problem

then the chaos genetic algorithm is used to solve the global optimization problem.Firstly

a hyperchaotic system is used to generate the initial population and the chaos disturbance vector to improve the genetic algorithm

then the network selection evaluation index weight can be obtained by solving the fitness function.The simulation results show that the newly arrived users can be distributed more evenly by using the algorithm.Meanwhile

the network blocking probability and the disparity of the blocking probability of different candidate networks can be reduced effectively

so that the network load is balanced.

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