Flexible genetic algorithm: a simple and generic approach to node placement problems

Yu-Hui Zhang, Yue-Jiao Gong, Tian-Long Gu, Yun Li, Jun Zhang

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
30 Downloads (Pure)


Node placement problems, such as the deployment of radio-frequency identification systems or wireless sensor networks, are important problems encountered in various engineering fields. Although evolutionary algorithms have been successfully applied to node placement problems, their fixed-length encoding scheme limits the scope to adjust the number of deployed nodes optimally. To solve this problem, we develop a flexible genetic algorithm in this paper. With variable-length encoding, subarea-swap crossover, and Gaussian mutation, the flexible genetic algorithm is able to adjust the number of nodes and their corresponding properties automatically. Offspring (candidate layouts) are created legibly through a simple crossover that swaps selected subareas of parental layouts and through a simple mutation that tunes the properties of nodes. The flexible genetic algorithm is generic and suitable for various kinds of node placement problems. Two typical real-world node placement problems, i.e., the wind farm layout optimization and radio-frequency identification network planning problems, are used to investigate the performance of the proposed algorithm. Experimental results show that the flexible genetic algorithm offers higher performance than existing tools for solving node placement problems.

Original languageEnglish
Pages (from-to)457-470
Number of pages14
JournalApplied Soft Computing Journal
Early online date27 Oct 2016
Publication statusPublished - 31 Mar 2017


  • genetic algorithm (GA)
  • node placement problem (NPP)
  • RFID network planning (RNP)
  • variable-length encoding
  • wind farm layout optimization (WFLO)


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