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

Zhang, Yu-Hui and Gong, Yue-Jiao and Gu, Tian-Long and Li, Yun and Zhang, Jun (2017) Flexible genetic algorithm : a simple and generic approach to node placement problems. Applied Soft Computing Journal, 52. pp. 457-470. ISSN 1568-4946 (https://doi.org/10.1016/j.asoc.2016.10.022)

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Abstract

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.

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