Optimal Anti-submarine Search Path for UUV via an Adaptive Mutation Genetic Algorithm
Unmanned underwater vehicle (UUV) is significant equipment for underwater anti-submarine operation. In this paper, the optimal anti-submarine search path for UUV is investigated through an adaptive mutation genetic algorithm (AMGA). The AMGA utilizes three control factors to dominate the direction and amplitude of mutation adaptively and to improve the convergence speed. The mathematical programming model for UUV optimal search is established by maximizing cumulative detection probability (CDP). The enemy submarine is described as Markovian target, and the search radius and search width of the UUV are considered. Reasonable and efficient search paths are obtained under different conditions. The results indicate that the optimal path for UUV is effective and suggestive for anti-submarine search.
KeywordsUnmanned underwater vehicle (UUV) Anti-submarine search Optimal path Adaptive mutation genetic algorithm (AMGA)
This research was partially supported by the National Natural Science Foundation of China (Grant Nos 61375055), the scholarship from China Scholarship Council (Grant No. 201506290080), the China Postdoctoral Science Foundation (Grant No. 2019M653652) and the Fundamental Research Funds for the Central Universities.
- 2.Qian, D., Zhao, J., Yang, Y.: Development trend of military UUV (II): a review of US military unmanned system development plan. J. Unmanned Undersea Syst. 25(3), 107–150 (2017)Google Scholar
- 3.Kumar, A., Kurmi, J.: A review on unmanned water surface vehicle. Int. J. Adv. Res. Comput. Sci. 9(2), 95 (2018)Google Scholar
- 4.Chen, P., Wu, X.: Optimal extended position call-search method for UUVs’ formation. Syst. Eng. Electron. 35(5), 987–992 (2013)Google Scholar
- 5.Chen, P., Wu, X.-F., Chen, Y.: Method of call-search for Markovian motion targets using UUV cooperation. Syst. Eng. Electron. 34(8), 1630–1634 (2012)Google Scholar
- 6.Li, B., Chiong, R., Gong, L.-G.: Search-evasion path planning for submarines using the artificial bee colony algorithm. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 528–535 (2014)Google Scholar
- 9.Cho, J.-H., Kim, J.S., Lim, J.-S., et al.: Optimal acoustic search path planning for sonar system based on genetic algorithm. Int. J. Offshore Polar Eng. 17(03), 218–224 (2007)Google Scholar
- 10.Zhang, X., Ren, Y., Wang, R.: Research on optimal search path programming in continuous time and space based on an adaptive genetic algorithm. Acta Armamentarii 36(12), 2386–2395 (2015)Google Scholar
- 11.Zhang, X., Ren, Y.-F., Shen, J.: Improved double chains genetic algorithm for optimal searcher path problem in continuous time and space. Syst. Eng. Electron. 37(5), 1092–1098 (2015)Google Scholar