基于粒子群优化、爬坡和分布式遗传算法的WMNs节点放置混合智能系统的设计与实现

Shinji Sakamoto, Admir Barolli, L. Barolli, M. Takizawa
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引用次数: 12

摘要

无线网状网络(Wireless Mesh Networks, WMNs)具有低成本、高速的无线互联网连接能力,正成为一种重要的网络基础设施。在我们之前的工作中,我们实现了一个基于粒子群优化(PSO)和爬山(HC)的混合智能系统,称为WMN-PSOHC,以及一个基于遗传算法(GA)的仿真系统,称为WMN-GA,用于解决wmn网络中的节点放置问题。在本文中,我们实现了一种基于PSOHC和分布式遗传算法(DGA)的混合仿真系统,称为WMN-PSOHC-DGA。通过计算机仿真对WMN-PSOHC-DGA系统进行了评价。仿真结果表明,与WMN-PSOHC-DGA相比,WMN-PSOHC-DGA系统具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation of a Hybrid Intelligent System Based on Particle Swarm Optimization, Hill Climbing and Distributed Genetic Algorithm for Node Placement Problem in WMNs: A Comparison Study
The Wireless Mesh Networks (WMNs) have low cost and high speed wireless Internet connectivity, therefore they are becoming an important networking infrastructure. In our previous work, we implemented a hybrid intelligent system based on Particle Swarm Optimization (PSO) and Hill Climbing (HC), called WMN-PSOHC, and a simulation system based on Genetic Algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a new hybrid simulation system based on PSOHC and distributed GA (DGA), called WMN-PSOHC-DGA. We evaluate WMN-PSOHC-DGA system by computer simulations. The simulation results show that the WMN-PSOHC-DGA system has a better performance compared with WMN-PSODGA.
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