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针对传统粒子群算法(PSO)状态估计搜索效率不高的问题,提出基于量子简谐振子粒子群(QHOP-SO)状态估计算法。该算法利用量子空间中不确定性原理,保证粒子满足聚集态从而在整个可行解空间进行搜索,避免程序陷入局部最优点。同时,将谐振子势能场引入粒子群系统,通过模拟经典简谐振动中势能状态的变化与量子振动中能级的跃迁,提高粒子的搜索效率。最后,利用内点罚函数法对约束条件进行处理,借助IEEE算例仿真验证所提方法的有效性,对不同算法进行比较,结果表明所提方法在全局收敛能力与搜索速度方面均具有明显的优势。
Aiming at the problem of low search efficiency of traditional Particle Swarm Optimization (PSO) state estimation algorithm, a QHOP-SO state estimation algorithm based on quantum simple harmonic oscillator particle swarm optimization (PSO) is proposed. The algorithm uses the principle of uncertainty in quantum space to ensure that the particles satisfy the aggregation state and search through the feasible solution space to avoid the program from falling into the local optimum. At the same time, the potential field of the harmonic oscillator is introduced into the particle swarm system, and the search efficiency of the particle is improved by simulating the change of the potential energy state in classical harmonic vibration and the level transition in the quantum vibration. Finally, the internal penalty function method is used to deal with the constraint conditions. The validity of the proposed method is validated by IEEE examples. The comparison of different algorithms shows that the proposed method has obvious global convergence ability and search speed Advantage.