Numerical simulation results show that, compared with QDPSO, it is effective, with strong ability to avoid being trapped in local minima and robust to initial value.
数值实验结果表明，与量子粒子群优化算法相比，该算法效率高、优化性能好，具有较强的避免局部极小能力，对初值具有较强的鲁棒性。
Based on QDPSO, a new algorithm is presented to tune PID controller parameters, which has superior features, including easy implementation, stable convergence, and quick computational efficiency.
基于QDPSO提出了一种新的PID参数整定算法，该算法具有操作简单、稳定收敛、寻优快速等优点。
Based on QDPSO, a new algorithm is presented to tune PID controller parameters, which has superior features, including easy implementation, stable convergence, and quick computational efficiency.
基于QDPSO提出了一种新的PID参数整定算法，该算法具有操作简单、稳定收敛、寻优快速等优点。
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