Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks |
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Authors: | Yong Liu Wei-Min Zheng Shangkun Liu Qing-Wei Chai |
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Institution: | 1.College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;2.Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;3.College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China |
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Abstract: | Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm. |
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Keywords: | heuristic algorithms fish migration optimization localization mobile sensor networks monte carlo localization |
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