A Novel Adaptive Genetic Algorithm for Mobility Management in Cellular Networks

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• 关键词：Evolutionary computation ; Adaptation ; Cellular networks
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9648
• 期：1
• 页码：225-237
• 全文大小：1,583 KB
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• 作者单位：Zakaria Abd El Moiz Dahi (17)
  Chaker Mezioud (17)
  Enrique Alba (18)
  
  17. Department of Fundamental Computer Science and Its Application, Constantine 2 University, Constantine, Algeria
  18. Dep. de Lenguajes y Ciencias de la Computación, Málaga University, Málaga, Spain
  
• 丛书名：Hybrid Artificial Intelligent Systems
• ISBN：978-3-319-32034-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Metaheuristics are promising tools to use when addressing optimisation problems. On the other hand, most of them are hand-tuned through a long and exhaustive process. In fact, this task requires advanced knowledge about the algorithm used and the problem treated. This constraint restricts their use only to pure abstract scientific research and by expert users. In such a context, their further application by non-experts in real-life fields will be impossible. A promising solution to this issue is the inclusion of adaptation within the search process of these algorithms. On the basis of this idea, this paper demonstrates that simple adaptation strategies can lead to more flexible algorithms for real-world fields, also more efficient when compared to the hand-tuned ones and finally more usable by non-expert users. Seven variants of the Genetic Algorithm (GA) based on different adaptation strategies are proposed. As benchmark problem, an NP-complete real-world optimisation problem in advanced cellular networks, the mobility management task. It is used to assess the efficiency of the proposed variants. The latter were compared against the state-of-the-art algorithm: the Differential Evolution algorithm (DE), and showed promising results.