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针对多目标粒子群优化算法在求解火力分配过程中容易陷入局部最优的问题,提出一种改进的多目标量子粒子群优化(Multi-Objective Quantum-Behaved Particle Swarm Optimization,MOQPSO)算法。通过改进编码方式、修改位置更新公式、引入高斯变异和更新外部档案等方法,使该算法适于求解多平台多武器火力分配多目标优化模型。对规模不同的2个作战想定分别采用改进MOQPSO算法和MOPSO算法进行求解。对多目标优化与单目标优化模型的收敛性能进行了比较。仿真结果表明:改进MOQPSO算法比MOPSO算法运算速度提高6倍左右,所求Pareto解的收敛精度更高、多样性更好,验证了所提算法的有效性和优越性。
To solve the problem that multi-objective particle swarm optimization (PSO) is easy to fall into the local optimum in the process of fire distribution, an improved multi-objective Quantum-Behaved Particle Swarm Optimization (MOQPSO) algorithm is proposed. By improving the coding method, modifying the position update formula, introducing Gaussian mutation and updating external files, the algorithm is suitable for solving multi-platform multi-target firepower distribution optimization model. Two battles of different sizes are supposed to be solved by the improved MOQPSO algorithm and MOPSO algorithm respectively. The convergence performance of multi-objective optimization and single-objective optimization models are compared. The simulation results show that the improved MOQPSO algorithm can improve the computational speed by about 6 times compared with the MOPSO algorithm, and the accuracy and the diversity of the Pareto solution are higher. The validity and superiority of the proposed algorithm are verified.