High Value Target Search Strategy Planning Based on MMA and Two Tier Architecture

Aiming at the problems of inefficient information utilization and insufficient participation of intelligent assistance in the field of high-value target search, a search strategy planning method based on modified mayfly algorithm (MMA) and two-tier architecture is proposed. By designing the Decision Layer and the Execution Layer, the traditional high-value target search problem is divided into two parts: strategy formulation and specific implementation, so as to reduce the complexity of search strategy planning. By improving the biologically inspired neural network decision based on vector weighting (VW-BINND), the diversity of deadlock temporary target point selection is improved. In order to improve the quality of search strategy planning, the fitness function is designed and mayfly algorithm (MA) is improved from the aspect of dance distance to improve the problems of slow convergence speed and easy to fall into local extremum in the process of selecting the parameters of temporary target points in common optimization algorithms, and solve the optimization problem of search strategy. The simulation results show that the high-value target search strategy planning method proposed has better planning quality, reduces the complexity of the search strategy on the basis of meeting the requirements of high-value target search, and also shows that the method is effective, advanced and accurate.