The standard particle swarm optimization algorithm as a random global search algorithm, because of its rapid propagation in populations, easily into the local optimal solution.
标准的粒子群优化算法作为一种随机全局搜索算法，因其在种群中传播速度过快，易陷入局部最优解。
A new random optimization algorithm based on population search is proposed.
提出了一种新的基于群体搜索的随机优化算法。
Based on the analysis of the performance index a new algorithm, two stage random search algorithm with variable radius, is put forward.
经对性能指标性质的分析给出了一种模糊神经网络的学习算法——二阶段变半径随机搜索法。
As a classical partition clustering algorithm, CLARANS USES local search with random restart to find clusters central points.
CLARANS算法是经典的划分聚类算法，其核心思想是采用随机重启的局部搜索方式搜索中心点。
This paper presents a trust path search algorithm based on random walk, which is able to improve the search by using the path information in the past.
传统的局部信任模型采用简单洪泛的方法获得信任信息，针对该方法效率较低且对网络资源消耗较大的问题，提出一种基于随机漫步的搜索信任路径的算法。
And the feasible path was obtained by using RRT algorithm to perform random search of path node.
采用RRT算法，通过随机搜索路径节点，得到可行姿态路径。
Results show that that the search ability of TIGA algorithm, whose random degree is neither the highest nor the lowest, is the best among the tested sister algorithms.
由实验结果可知，随机性适中的TIGA算法对各测试函数的寻优能力最好。
We made improvement to the search mechanism of the traditional Ant Colony Algorithm(ACA), put forward a Random Ant Colony Algorithm(RACA), and applied it in air combat decision.
用其他随机优化算法（模拟退火算法、遗传算法、进化规划等）与免疫算法进行了比较研究，给出了他们的异同点、免疫算法的优点等。
We made improvement to the search mechanism of the traditional Ant Colony Algorithm(ACA), put forward a Random Ant Colony Algorithm(RACA), and applied it in air combat decision.
用其他随机优化算法（模拟退火算法、遗传算法、进化规划等）与免疫算法进行了比较研究，给出了他们的异同点、免疫算法的优点等。
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