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.
Original language | English |
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Pages (from-to) | 457-470 |
Number of pages | 14 |
Journal | Applied Soft Computing Journal |
Volume | 52 |
Early online date | 27 Oct 2016 |
DOIs | |
Publication status | Published - 31 Mar 2017 |
Funding
This work was supported in part by the National Natural Science Foundation of China (NSFC) Key Project under Grant 61332002, in part by the NSFC Youth Project under Grant 61502542, and in part by the NSFC Joint Fund with Guangdong Key Projects under Grant U1201258.
Keywords
- genetic algorithm (GA)
- node placement problem (NPP)
- RFID network planning (RNP)
- variable-length encoding
- wind farm layout optimization (WFLO)