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。
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.
它们均是一种随机搜索的迭代算法，对优化对象的性态无要求。
They are a kind of stochastic search iterative algorithm and no demand of optimization object.
传统的最优化技术大多是基于梯度寻优技术或随机搜索的方法。
Traditional optimization techniques search for the best solutions using gradients or random searching.
模拟进化和模拟退火是解决全局优化问题的随机搜索技术，它们在工程领域有着广泛的应用。
Simulated evolution and simulated annealing are two stochastic search algorithms for solving the global optimization problems. They have been widely used in different engineering areas.
提出了一种新的基于群体搜索的随机优化算法。
A new random optimization algorithm based on population search is proposed.
它们均是随机搜索的迭代算法，对优化对象的初始状态无要求。
They are stochastic search iterative algorithms and no demand of optimization object.
将基于直接搜索法的随机全局优化方法用于求解该问题的全局最优解，给出了具体的算法步骤。
A stochastic global optimization method based on direct search is introduced to solve the global optimal solution of the problem, and the process is also discussed in detail.
启发式随机搜索策略和局部优化算法相结合的求解方案是解决复杂函数优化的有效途径。
Combining a heuristic random searching strategy with local optimal algorithms is effective solution for complex optimization problem.
该算法本质上是一种随机搜索算法，并能以较大概率收敛到全局最优，特别适用于连续函数的优化。
The algorithm is a random searching algorithm in nature. It can converge to the global minima more probability and be adept in continuous functions optimization.
粒子群优化算法是一种基于种群搜索策略的自适应随机算法。
Particle swarm optimization is a kind of self-adaptive random algorithm based on group hunting strategy.
蚂蚁算法是一种新兴的随机搜索优化算法在近几年。
Ant algorithm is a newly emerged stochastic searching optimization algorithm in recent years.
搜索技术，诸如遗传算法，模拟退火算法，禁忌搜索和随机移动算法，已经广泛应用于全局优化。
Search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Random Walk Algorithms have been used extensively for global optimization.
应用NL J随机搜索算法，对催化裂化控制回路进行基于阶跃响应的PID参数优化和IMC - PID参数优化。
Using NLJ search procedure to optimize the control loops of FCCU plant by selecting PID parameters based on IMC control principle and step response data.
通过改进遗传算法，提出一种求解全局优化问题的变异基随机搜索方法。
Presents mutation principles based random restart heuristics for the global optimization problem;
在求解随机最优路径方面，其问题分两种情形：一种以成功概率为优化目标，搜索约束条件下完成运输任务最大概率的路径；
Two scenarios are considered: one sets the success probability as the objective and tries to find out the route with the maximum probability subject to time constraint;
在求解随机最优路径方面，其问题分两种情形：一种以成功概率为优化目标，搜索约束条件下完成运输任务最大概率的路径；
Two scenarios are considered: one sets the success probability as the objective and tries to find out the route with the maximum probability subject to time constraint;
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