A hybrid meta-heuristic method for the optimized deployment of the multi-unmanned underwater platforms

A novel hybrid meta-heuristic method is proposed in this paper for the optimized deployment problem of cooperative detection of multi-unmanned underwater platforms. First, a detection model considering fault-tolerant radius is employed to accurately describe the detection performance of underwater unmanned platforms. The optimized deployment model of the unmanned system is established by combining the detection coverage and communication energy consumption. Second, the Latin Hypercube Sampling(LHS) method is used to initialize the population to improve the quality of the initial population. Next, a novel hybrid meta-heuristic method is proposed. The optimal parameters are solved by Kriging interpolation method to improve the computational accuracy of the method. Finally, the analysis results of the simulation experiments show that the unmanned platform detection model is effective. Moreover, the hybrid meta-heuristic method is capable of the optimized deployment task of cooperative detection of multi-unmanned underwater platforms, and its comprehensive performance is better than that of comparison algorithms.