Research on multi-task assignment method of multi-configuration UAV based on improved hybrid algorithm

Le résumé fourni par la source

In this paper, an improved hybrid algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) is formed to solve the problem of "premature" and slow convergence. First of all, this paper improves the multi-task Collaborative assignment model (CMTAP), comprehensively considers the task completion, sequence, time, UAV type, quantity, energy consumption and other factors, and establishes a scientific and standardized assignment model that meets the task requirements. Secondly, for the problem of target allocation, this paper proposes an improved hybrid particle swarm optimization algorithm (PSO), which is based on particle swarm optimization (PSO), and uses genetic algorithm (GA) to increase the global search ability of the algorithm for the problem that the optimization of discrete nonlinear functions is very easy to fall into the local optimal solution. The mixed coding method of traditional coding and binary coding is used to solve the problem of uncertain number of unmanned aerial vehicles (UAVs). At the same time, the PSO algorithm is reduced by increasing the inertia weight, increasing the time-varying factor, and adopting the adaptive mutation probability, bit mutation and cross mutation methods for GA to increase the convergence speed and global search ability of the hybrid algorithm. Finally, the overall cost of the improved hybrid algorithm, hybrid algorithm and traditional algorithm is compared by simulation, and the effectiveness and accuracy of the proposed algorithm are verified.

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