引入人工蜂群搜索算子的QPSO算法的改进实现

针对基于人工蜂群搜索算子的量子粒子群算法（IQPSO）求解精度不理想，收敛速度慢等问题，将一种更新全局最优的新策略融入到IQPSO算法中，引入双中心粒子，将IQPSO算法得到的全局最优解进行多种群划分，使得全局最优解的每一维度的值都与双中心粒子相对应的维度分别替换，再次更新全局最优，在算法解附近探索更加精确的结果。通过五个测试函数的仿真实验与IQPSO算法比较，验证所提的算法有良好的准确性与收敛速度的改进。

Realization of improved Quantum Particle Swarm Optimization algorithm based on search operator of artificial bee colony

For the Quantum Particle Swarm Optimization algorithm based on artificial colony search operator（IQPSO） precision is not ideal and slow convergence speed, this paper combines a new strategy of updating the global optimal with IQPSO algorithm, introduces double center particle and makes the global optimum solutions for each dimension which are replaced with double center particle dimension corresponding respectively to update the global optimum again, near the algorithm solution to explore more accurate results. Through the five test functions compared with IQPSO algorithm, simulation experiments validate the proposed algorithm has better accuracy and faster convergence speed.