对于高维复杂函数，一般粒子群优化算法收敛速度慢，易早熟收敛。
For complex functions with high dimensions, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima.
对于高维复杂函数，传统的确定性算法易陷入局部最小，而单一的全局随机搜索算法收敛速度慢。
For complex functions with high dimensions, canonical optimization methods are easy to be trapped in local minima and simple random search methods are slow on convergence.
通过混合使用多种杂交算子并辅之以间歇变异，提出了一种求解高维复杂函数全局优化问题的新型演化算法。
A new evolutionary algorithm based on hybrid crossovers and intermittent mutation for global optimization of complex functions with high dimensions is proposed.
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