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针对量子粒子群算法后期收敛速度慢,且易陷入局部最优解等问题,将粒子种群全局最优替代的思想和多种群并行搜索策略相结合,设计了一种基于含局部种群变异的多种群并行搜索替代的量子粒子群算法。该算法采用种群并行搜索替代的方式引入变异,增加了种群的多样性,同时在局部种群中利用全局最优替代平均最优,加快收敛速度。分别应用基本粒子群、量子粒子群和种群替代的量子粒子群算法对某含风电网进行仿真,验证了种群替代的量子粒子群算法在含风电网无功优化中的有效性和实用性。
Aiming at the problems of slow convergence speed and easily falling into local optimal solution in the later period of quantum particle swarm optimization, a new method based on the idea of global optimal replacement of particle swarm and multi-swarm parallel search strategy is proposed. Parallel Search for Alternative Quantum Particle Swarm Optimization. In this algorithm, mutation is introduced by means of population parallel search substitution, which increases the diversity of the population. At the same time, global optimization is used to replace the average optimality in local population to speed up the convergence. The particle swarm optimization algorithm based on quantum particle swarm optimization (PSO), quantum particle swarm optimization (PSO) and population replacement is used to simulate a wind power grid. The effectiveness and practicability of the PSO algorithm based on wind farms are verified.