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- 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。
- 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。
- 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。
- Considering the premature convergence limitation of the basic particle swarm optimizer (PSO) algorithm, an improved adaptive PSO algorithm with mutation was proposed. 针对粒子群算法易早熟收敛的局限性，提出了一种带变异的改进自适应粒子群优化(PSO)算法。
- An improved PSO (particle swarm optimization) algorithm is presented which well addresses slow convergence speed and low calculation precision in the basic PSO algorithm. 摘要提出了一种改进的粒子群算法，很好地解决了基本粒子群算法中易陷入局部最优的缺点。
- To improve the searching performance of Particle Swarm Optimization (PSO), a modified PSO algorithm with flying time adaptively adjusted was proposed and named FAA-PSO algorithm. 为改善粒子群优化算法的搜索性能,提出一种飞行时间自适应调整的粒子群算法(FAA-PSO)。
- In order to overcome the shortcomings that standard Particle Swarm Optimization（PSO） traps into local optima easily and has a low convergence accuracy，an improved PSO algorithm is proposed. 摘要 针对标准粒子群算法容易陷入局部最优、收敛精度低的缺点，提出了一种改进的粒子群算法。
- 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。
- 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。
