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A multi‐objective optimization model based on immune algorithm in wireless mesh networks
Author(s) -
Chen Jing,
He Kun,
Du Ruiying,
Yu Fajiang,
Yuan Quan,
Wang Lina,
Fu Cai
Publication year - 2014
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2808
Subject(s) - computer science , redundancy (engineering) , performance metric , wireless mesh network , interoperability , metric (unit) , quality of service , mathematical optimization , multi objective optimization , throughput , wireless network , distributed computing , wireless , computer network , machine learning , telecommunications , operations management , mathematics , management , economics , operating system
Summary With the characteristics of high self‐organized, dynamic, and interoperable, the wireless mesh network (WMN) is deemed as a potential technology to be applied widely for home, enterprise, and social public service. Many current optimization schemes usually focus on a single metric such as network deployment cost, throughput, QoS, and so on, but few schemes consider that the optimized metric may affect other metrics of WMN. In practice, the influence among the different metrics is often nonignorable. To optimize the performance from a global perspective, we propose a multi‐objective optimization model based on immune algorithm (MOM‐IA), which provides a paradigm to find the optimal solution satisfying some different restriction conditions. To simplify, MOM‐IA mainly analyzes the restriction relationship of connectivity, redundancy, and throughput, which are the multiple objects. Considering the characteristic of dynamic and the discrete integer parameters in WMN, we design a longtime evolution immune algorithm to solve the MOM. Finally, the analysis of experiments presents that MOM‐IA has good performance in solution set diversity and Pareto‐front distribution, which means the probability to find the optimal solution in WMN. Copyright © 2014 John Wiley & Sons, Ltd.