演化学习具有全局优化性能好，实验样本数要求少等优点，但也存在一些问题，如演化学习的速度较慢、模型的稳定性较差等。

But it has some disadvantages, for example:the rate of evolutional learning is too slow sometimes, and the stability of network is discontented.

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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。

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.

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