Optimal Anti-submarine Search Path for UUV via an Adaptive Mutation Genetic Algorithm

Abstract

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.