舰船电子对抗

针对组网雷达的多干扰机协同干扰，在解决多目标组合优化问题的前提下，为解决分配资源规模过大，算法容易产生局部收敛的问题，提出一种基于新的个体更新方式的鲸鱼优化算法(WOA)。个体之间编码通过字符交叉与优化目标对比的方式产生，防止传统编译码更新过程中由于编译规则不充分而出现问题，保留原编码中关键字符，增强原算法的寻优能力。通过鲸鱼群中的最优个体与最劣个体来决定鲸鱼优化算法中的更新次数，保证算法在一定程度上有跳出局部收敛的能力，提升种群的多样性，从而进一步提高算法的寻优能力。仿真结果表明，和其他群智能算法相比，改进的鲸鱼群优化算法(IWOA)收敛稳定性更高，全局寻优能力更出色，能够解决多目标组合优化问题。

For the cooperative jamming of multiple jammers to networked radar system, under the premise of solving multi-objective combinatorial optimization problem, to solve the issue that excessively large resource allocation scales often leads to local convergence issues, a new individual updating method based whale optimization algorithm(WOA) is proposed.The encoding between individuals is generated through character crossing and comparison with optimization objectives, which prevents the issues that may arise during the traditional encoding update process due to insufficient compilation rules, while the key characters in the original encoding are retained to enhance the optimization capability of the original algorithm.The number of updates within the whale optimization algorithm is determined by the best and worst individuals in the whale pod, which ensures the algorithm has the ability to escape local convergence to a certain extent and enhances the diversity of the population, thereby the algorithm's optimization capability is further improved.Simulation results show that, compared with other swarm intelligence algorithms in recent years, the improved whale optimization algorithm(IWOA) has higher convergence stability and superior global optimization capability, and it can solve multi-objective combination optimization problems.