认知无线电网络遗传-蚁群联合优化路由算法  

Cognitive radio networks genetic-ant colony joint optimization routing algorithm

作　　者：陈善学[1] 张佳佳[1] 朱江[1] 郑文静[1] 

机构地区：[1]重庆邮电大学移动通信技术重庆市重点实验室,重庆400065

出　　处：《计算机工程与设计》2015年第4期886-891,共6页Computer Engineering and Design

基　　金：国家自然科学基金项目(61102062);教育部科学技术研究重点基金项目(212145);重庆市科委自然科学基金项目(CSTC2011JJA1192);重庆邮电大学博士启动基金项目(A2010-11);重庆市教委科学技术研究基金项目(KJ120530)

摘　　要：为保证信息的实时性,改善网络的拥塞现象,对如何有效减少信息传递过程中的时延并增大网络吞吐量进行研究,对蚁群算法(AC)收敛性和搜索范围方面存在的不足进行分析,提出一种遗传-蚁群联合优化算法(GACA)。遗传算法(GA)与AC算法相结合,利用GA算法求出较优的初始信息素,提高GACA算法的收敛速度,提出一种自适应搜索方式有效避免局部收敛,提高算法的全局寻优能力。仿真结果表明,GACA算法的实时性以及网络吞吐量均优于AC算法。To ensure the real-time information and improve the network congestion,the issues that how to effectively reduce information delay during the transmission process and increase the throughput of the network were studied.To overcome shortcomings of ant colony algorithm（AC）in terms of convergence and search range,agenetic-ant colony joint optimization algorithm（GACA）was proposed.To improve the convergence rate of GACA algorithm,genetic algorithm（GA）was combined with AC algorithm and the better initial pheromone was solved.An adaptive search method was proposed to effectively avoid local convergence,thereby enhancing the global-optimization capability of the proposed algorithm.Results of the simulation show that the real-time and network throughput of GACA algorithm are better than that of AC algorithm.

关 键 词：时延 吞吐量 遗传算法 蚁群算法 自适应搜索方式 

分 类 号：TN915.04[电子电信—通信与信息系统]