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.
桁架拱结构是一个高次超静定的复杂三维结构，构件的功能函数没有明确表达式。
The truss arch structure is a three-dimensional higher statically indeterminate structure, and no definite expression of the performance function can be given for its unbelievable complexity.
针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。
Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.
针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。
Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.
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