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- An improved PSO algorithm was presented and applied to optimal the PID parameters of electromotor. 为此,提出一种改进的PSO优化算法,并将该算法应用于电机控制系统的PID参数优化设计。
- It is showed that the PSO algorithm is an efficient method to deal with NLP problems. 结果表明 ,PSO算法在使用的普遍性、求解的准确性方面都优于一般的算法 ,是一种有效的求解NLP问题的方法
- At last, applications of PSO algorithm are discussed, and further research issues and some suggestions are given. 最后归纳了PSO算法的应用概况，并就PSO算法进一步的研究工作进行了探讨和展望。
- The PSO algorithm is improved to make it more intelligent, which is used in mute planning of the cruise missile. 对近年来出现的粒子群算法进行了改进以使其更具智能化，将它应用于巡航导弹路径规划问题，并进行了仿真计算。
- The experimental results show that the PSO algorithm provides an effective method to estimate parameters of NSM. 实验结果表明：微粒群算法为非线性系统模型参数估计提供了一种有效的途径。
- The results of simulation show that the proposed PSO algorithm has its high validity, robustness and efficiency. 仿真结果表明，改进的PSO算法有更好的搜索效率，取得了较好的效果。
- Traditional analog filter is imprecise and inefficient, The optimal parameters of the filters can be obtained by introducing the PSO algorithm. 传统的模拟滤波器的精度与效率均较差，引入PSO算法可对滤波器参数进行寻优。
- Some illustrating results show that the PSO algorithm is usable and valid for both structural model updating and structural damage detection. 由此可见，PSO算法应用于该领域的效果是显而易见的。
- If some particles trended to local extremum in PSO algorithm implementation, the particle velocity was updated and re-initialized. 在PSO算法的运行过程中，对有集聚倾向的粒子进行速度变异处理，重新初始化速度。
- The adoption of niche concept improves the ability of PSO algorithm in solving multimodel function optimization problems. 小生境技术的引入,提高了微粒群算法处理多峰函数优化问题的能力。
- A novel two-swarm based PSO algorithm (TSPSO) with roulette wheel selection was proposed to solve optimal power flow problem. 提出一种带赌轮选择的双种群粒子群优化算法(TSPSO)求解最优潮流问题。
- To get better optimization results, an improved PSO algorithm named IPSO including variance mechanism and local updating mechanism was presented. 为提高其优化求解效果，引入变异机制及局部更新机制对粒子群优化算法进行改进。
- PSO is simple and easy to implement, but the value of inertia parameter and the swarm size have an important effect on the PSO algorithm performance. 摘要PSO的优点是算法表达简单，易于实现，其中的惯性权重参数选择和种群大小选择对算法特性有显著影响。
- Abstract: A novel two-swarm based PSO algorithm (TSPSO) with roulette wheel selection was proposed to solve optimal power flow problem. 摘 要： 提出一种带赌轮选择的双种群粒子群优化算法(TSPSO)求解最优潮流问题。
- An improved fuzzy adaptive PID algorithm (IFPID) is thus proposed, using PSO algorithm to optimize the preprocessed membership function. 针对这种情况，提出改进的模糊自适应PID控制算法(IFPID)，利用微粒群算法对经过预处理的隶属度函数进行优化。
- The PSO algorithm is a new evolutionary computation method which is applicable to complex optimization problems that are nonlinear, nondifferentiable and multimodal. 微粒群算法是一种新兴的算法,它能有效地解决非线性、不可微、多峰的复杂优化问题。
- To enhance the diversity of swarm and improve the global convergence of PSO algorithm, PID controller is introduced to control dynamic evolutionary behavior of DEPSO algorithm. 在此基础上，通过引入PID控制器以控制DEPSO算法的动态进化行为，以增强微粒产生的多样性，从而改进微粒群优化算法的全局收敛性。
- The test results based on three benchmark functions show that the improved PSO algorithm has a good performance on global convergency and convergence precision. 基于3个基准测试函数的测试结果显示改进粒子群优化算法具有较好的全局收敛性和收敛精度。
- Particle swarm optimization is an effective random and holistic optimization algorithm，but the classical PSO algorithm easily plunges into local minimums. 摘要 粒子群算法是一类有效的随机全局优化算法，但是经典PSO算法容易陷入局部最小值。
- Computational results show that the PSO algorithm is superior to the DPSO algorithm and is a useful method for solving the problem with 50 or less jobs. 实验结果证明，我们所提出的PSO演算法优于DPSO演算法，且对于50个工件以内的问题有很好的求解效果。
