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Research on cluster attack mission planning of multi USVs based on DAM-BBOPSO algorithm

机译:基于DAM-BBOPSO算法的多用途无人机集群攻击任务计划研究

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The rapid development of modern defense technology decreased the USVs' attacking effect greatly, autonomous formation cluster attack techniques of USVs has became one of the key technologies of future naval warfare, mission planning among USVs is the key for them to complete tasks smoothly and efficiently. Regarding the cluster attack mission planning problem as multi-constrained task allocation process, building mission planning model, and improve particle initialization and optimization process combined with distributed auction mechanism(DAM) and Biogeography-Based Optimization algorithm(BBO). Simulation result indicates that the program achieved with distributed auction mechanism particle swarm optimization could fully meet the requirements of USVs' cluster attack missions, and shows better convergence compared with traditional particle swarm optimization(PSO) and other swarm intelligence algorithms.
机译:现代防御技术的飞速发展大大降低了USV的攻击效果,USV的自主编队集群攻击技术已成为未来海军作战的关键技术之一,USV之间的任务计划是他们顺利高效完成任务的关键。将集群攻击任务计划问题作为多约束任务分配过程,建立任务计划模型,并结合分布式拍卖机制(DAM)和基于生物地理学的优化算法(BBO),改进粒子的初始化和优化过程。仿真结果表明,采用分布式拍卖机制的粒子群优化算法可以完全满足USV机群攻击任务的要求,与传统的粒子群优化算法(PSO)和其他群智能算法相比,具有更好的收敛性。

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