Research On The Application Of Optimization Algorithm In Multi-Station Jamming Resource Allocation

To get the best jamming effect of limited jamming resources in electronic warfare,this paper focuses on the problem of jamming resource allocation.By using three improved intelligent optimization algorithms,the solution to interference resource allocation problems under different models is achieved.The specific research contents are as follows:(1)Based on the suppression probability of jammers,an interference resource allocation objective function is established,and an improved war strategy algorithm(IWSO)is designed.The algorithm is improved in terms of coding,defense strategy,and neighborhood movement strategy.The improved algorithm is then used to solve the interference resource allocation model,obtain the globally optimal allocation strategy,and analyze and validate various interference resource allocation strategies.In the simulation experiments,the experimental results of the algorithm are compared with those of other similar models,and the results demonstrate that the algorithm achieves good average optimal values of the objective function and optimal allocation strategies,whether the jammer interferes with a single target or multiple targets.This provides a new solution for interference resource allocation problems based on the suppression probability.(2)Considering the limited energy of our jamming resources in actual combat,a multiobjective joint optimization model based on interference effectiveness and energy consumption of jammers is proposed.The model aims to maximize the interference effectiveness while minimizing the energy consumption of jammers,taking into account the requirements and constraints of actual combat.The model is solved using an improved multi-objective pelican optimization algorithm(MOIPOA),which includes improvements in population encoding,cross-mutation ideas,non-dominated sorting algorithm,and crowding distance calculation.Finally,the experimental results are compared with benchmark multi-objective optimization algorithms,namely NSGA-Ⅱ and MOBPSO,and the final results are analyzed and validated.The results demonstrate that the MOIPOA algorithm has better optimization ability in dealing with this problem,achieving dual-objective optimization in interference resource allocation.(3)Based on the dynamic battlefield environment,this paper innovates on the basic model by incorporating the jamming pattern of jammers.A matrix of jamming pattern influence factors is constructed,and a weight function related to target distance is established.The dynamic environment-based multi-pattern interference resource allocation model is proposed.The model is solved using an improved particle swarm optimization algorithm(IPSO),which includes improvements in population encoding,population initialization,and the introduction of probability-based elite retention strategy.The experimental results are then compared with other popular algorithms in the same category,namely particle swarm optimization,genetic algorithm,and an improved ant colony algorithm.The results demonstrate that the proposed algorithm has stronger optimization ability.Finally,the interference strategies obtained by the algorithm are verified and analyzed.The experimental results show that the interference strategies derived from the algorithm have a relatively large impact on the detection probability of radar at the same distance,and achieve better interference effects.The effectiveness of the model and the feasibility of the proposed approach are also validateds.