基于改进量子粒子群优化算法的多UCAV协同任务分配研究

Cooperative Task Allocation for Multiple UCAV Based on Improved Quantum-Behaved Particle Swarm Optimization Algorithm

针对多无人作战飞机协同任务分配问题建立了一种扩展的多目标整数规划模型,采用改进的量子粒子群算法求解最优方案;该算法在量子粒子群优化算法(QPSO)的基础上,利用混沌机制和变异算子来提高算法的多样性,在保证QPSO算法收敛速度的同时提高算法的寻优能力,克服了QPSO易陷入局部极小值的缺点;最后对算法进行了仿真,仿真结果验证了所提方法的可行性和有效性。

Based on the problem of cooperative task allocation for multiple UCAVs, this paper established an extension of Multi-Objective Integer Programming （MOIP） model and employed the Improved Quantum- Behaved Particle Swarm Optimization algorithm（IQPSO） to solve the optimal program. IQPSO was built on the basis of Quantum-Behaved Particle Swarm Optimization algorithm （QPSO）, and chaotic mechanism and variation operator were used to improve the variety of the algorithm. Comparing with the QPSO, this algo- rithm enhanced the ability of getting the optimal solutions so that avoiding entrapping the local minimum value while ensuring the algorithm＇ s convergence speed. The result of the simulation experiment proves the effectiveness and feasibility of the way introduced.