协同多目标攻击的混合蛙跳融合蚁群算法研究

摘要

Based on the problem of target assignment in BVR(Beyond Visual Range)air combat, a Shuffled Frog Leaping Algo-rithm(SFLA)and Ant Colony Algorithm(ACA)fusion is presented. A model of decision-making in BVR is built up by taking target threat evaluation as the criterion. According to the characteristic of BVR, a special coding process for frog is presented. An improved SFLA based on mutation idea in Differential Evolution(DE)is proposed, and the aberrance operator for ACA is embedded to reduce the search time. Since shuffled frog leaping algorithm has the capability of taking a global searching rapidly and ant colony algorithm has the positive feedback feature, the fusion algorithms use the SFLA to build optimized group at its initial stage, and then use ACA to search the exact answer at the later stage. With Matlab, simulations are implemented. The simu-lation results show that this method can give a reasonable target allocation plan effectively.%针对未来超视距条件下的多机协同空战，提出了一种基于混合蛙跳融合蚁群算法的目标分配方法。以目标威胁评估值为准则建立空战决策模型，根据空战决策特点对青蛙粒子进行特殊编码处理，在混合蛙跳算法局部搜索过程中加入自适应差分扰动机制、在蚁群算法中引入变异算子以减少算法搜索时间。融合算法利用混合蛙跳算法快速的全局搜索能力生成初始优化解群，利用蚁群算法具有正反馈的特点求精确解，利用Matlab仿真。仿真结果表明该方法能够快速有效地给出合理的目标分配方案。