By borrowing the ideas of population from genetic algorithms, we introduce an adaptive random search in quasi_Monte Carlo method (AQMC) for global optimization.
借用遗传算法中种群的概念，介绍了一种解全局优化的拟蒙特卡罗自适应搜索算法。
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。
The hunting task for multiple mobile robots in an unknown environment is modeled as five states, namely, lining up, random search, surrounding, catch, and prediction states in the paper.
为了实现多移动机器人在未知环境中的围捕，本文将任务建模为排队、随机搜索、包围、捕捉和预测五种状态。
The hunting task for multiple mobile robots in an unknown environment is modeled as five states, namely, lining up, random search, surrounding, catch, and prediction states in the paper.
为了实现多移动机器人在未知环境中的围捕，本文将任务建模为排队、随机搜索、包围、捕捉和预测五种状态。
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