A cooperative jamming resource allocation method based on PSO-SSNOA - Wei Liu; You Chen; Xiaohong Zhao; Dejiang Lu; Tianjian Yang (AIP Advances)
A critical challenge degrading the survival probability of penetrators in air penetration missions against networked radar systems stems from inefficient jamming resource allocation, particularly in decision-making quality and real-time responsiveness. To address this challenge, we first construct a many-to-many jamming resource allocation model for jammer formations countering networked radar systems, based on the operational scenario where multiple jammers escort penetrators. The objective function and constraint conditions of this model are rigorously formulated. Subsequently, we propose a Particle Swarm Optimization Guided Seasonal Strategy Nutcracker Optimization Algorithm (PSO-SSNOA) based jamming resource allocation method. This approach innovatively integrates Particle Swarm Optimization (PSO) with the Nutcracker Optimization Algorithm (NOA), while enhancing the nutcracker’s behavioral patterns through seasonal adaptation strategies. Extensive simulation results substantiate PSO-SSNOA’s triple advantage: (1) demonstrating 100% stable convergence probability that surpasses NOA’s 80% while matching PSO’s reliability; (2) achieving 76% faster average runtime than standard PSO implementation; and (3) requiring 90% fewer convergence iterations compared to conventional NOA methodology. These quantified metrics confirm the algorithm’s enhanced stability, real-time responsiveness, and optimization efficacy in complex electronic warfare environments.