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.
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.
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。
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