Optimal allocation of cooperative jamming resource based on hybrid quantum‐behaved particle swarm optimisation and genetic algorithm

Abstract

The multi-dimension jamming resources allocation (JRA) problem is studied to enhance the jamming effectiveness for the cooperative jammers formation against radar net. The allocation strategies include the methods of allocating jamming resources and the mode selection for the jamming signal. First, jamming resource optimum allocation model is established based on the detection probability of the netted radar fusion centre. Second, a hybrid quantum-behaved particle swarm optimisation and self-adjustable genetic algorithm (HQPSOGA) is proposed to optimise the deployment of the jamming resource innovatively with multi-constrained conditions. Finally, the HQPSOGA is compared with the integer-value genetic algorithm, standard particle swarm optimisation (PSO) and quantum-behaved PSO in JRA problem regarding the solution quality, robustness, convergence rate and reliability by a general Monte Carlo simulation. Simulation results show that the proposed method is capable of developing better overall interference capacity efficiently for the jammers formation than any other tested algorithm.