共查询到19条相似文献，搜索用时 546 毫秒
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粒子群优化(PSO)粒子滤波算法比较而言,容易陷入局部最优,因此算法的准确度就会降低。本文在解决该问题时引入了一种基于鱼群的PSO粒子滤波算法。此算法是通过鱼群优化在采样过程中找到全局最优的相对位置,使其能够向全局最优的位置逐渐接近,同时提高估计有效的粒子数目,从而能够更有效的减少粒子退化与枯竭问题。实验表明,此算法与PSO粒子滤波算法相比,在估计准确度方面有了较大的提升。 相似文献
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研究了考试系统中一个关键的技术——组卷算法,提出了应用粒子群优化算法组卷。PSO算法源于蜜蜂群觅食,是一种智能寻优算法,它首先初始化一群随机粒子（随机解）,然后通过迭代找到最优解。PSO算法组卷是一个离散性求解问题,这是对PSO算法应用上的一个扩展。 相似文献
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在电力系统领域,粒子群优化算法(PSO)被广泛应用于实现电能优化、电压控制、以及电容器优化配置等问题。但现有的PSO算法在电力系统优化应用中容易过早收敛,不能得到精确解。文章根据电网最优潮流具有典型的有约束、非线性的特点,提出了一种应用于电网经济调度问题的快速有效的求解方法,即改进粒子群(PSO)算法。从模仿生物遗传进化的角度出发,在参考现有PSO算法的基础上设计一种可以随适应度变化而变化的PSO算法模型。通过该算法与标准PSO算法在IEEE30节点系统上进行算法检验比较,可以清楚地看到所改进的算法在求解电网系统经济调度问题方面所具有的优越性能。 相似文献
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差分进化算法(Differential Evolution Algorithms,DE)和粒子群算法(Particle Swarm Optimization,PSO)是一种基于种群的全局优化的通用的启发式算法,已经用来求解很多的问题。本文提出一种基于DE和PSO的双进化方式的种群进化策略。对于种群中的每个粒子可以随机选择按照差分进化或者按照粒子群进化。为了提高进化的收敛速度,对于每一代粒子选择一个最优的粒子提供给按照PSO算法进化的粒子使用。通过4个标准函数测试该算法并与PSO和DE算法进行比较,实验证明该算法是一种求解精度高,速度快,鲁棒性好的算法。 相似文献
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针对标准的BP神经网络对于声音信号识别率不高的问题,提出了一种用粒子群算法（PSO）优化BP神经网络的算法,建立了声音信号识别模型。PSO优化BP神经网络主要是用PSO来优化BP神经网络的初始权值和闽值,然后通过训练BP神经网络得到识别模型的最优解,优化后的神经网络具有误判率小、反应速度快等特点。在实验中把标准的BP神经网络和PSO优化后的BP神经网络用于八种异常声音的MFCC特征量和差分MFCC特征量识别,结果表明：在声音信号的识别系统中采用PSO优化BP神经网络的算法提高了系统的识别性能,达到了系统设计的目的。 相似文献
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利用粒子群优化算法（PSO）强大的全局搜索能力,提出了用PSO算法优化FCM聚类中心的方法,有效地避免了传统的FCM由于初始值及噪声的影响,容易陷入局部最优的不足,同时图像分割的效果得到了提高,性能比传统的FCM方法更加稳定。实验结果表明,该方法在图像分类中的应用效果较好。 相似文献
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对标准PSO算法进行分析的基础上,针对PSO算法中的早熟收敛问题,提出了一种基于混沌序列的PSO算法（CPSO）。CPSO算法能够保证粒子种群的多样性,使粒子能够有效进行全局搜索;并以典型的基准优化问题进行了仿真实验,验证了CPSO的有效性。 相似文献
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针对基本粒子群优化算法(简称PSO)存在的早熟收敛问题,提出了一种既保持粒子活性又保证粒子快速收敛于全局极值点的改进粒子群优化(XARPSO)算法。在算法运行过程中,如果种群多样性逐步减小,直至超出下限时,种群不再向整体最优位置靠近,而是纷纷远离该最优位置,从而执行了"扩散"操作,而当种群多样性逐步增大,直至超出上限时,种群又开始向整体最优位置靠拢,即执行了"吸引"操作,从而保持了粒子的多样性。同时,该方法引入收缩因子的概念,即通过正确选择惯性权重系数与加速常数即学习因子这些控制参数的值的方法,确保算法收敛。 相似文献
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电网故障诊断的基本思想是根据保护动作原理将故障诊断问题表示为0-1规划问题。为了保证电网故障诊断的准确性和实时性,提出了一种改进的人工鱼群算法——二进制人工鱼群算法。分析了人工鱼群群聚行为和追尾行为最优方向的前进速度。并在此基础上与遗传算法、粒子群算法和量子免疫算法作了对比分析。结果表明:追尾行为最优方向的前进速度优于群聚行为,二进制人工鱼群算法综合性能优于遗传算法、粒子群算法和量子免疫算法。研究表明二进制人工鱼群算法具有收敛速度快、种群规模小和搜索能力强的特点。 相似文献
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粒子群优化（PSO）算法是一种模拟自然生物群体（swarm）行为的优化技术。PSO算法源于对鸟群觅食行为的研究,该算法简单易实现,可调参数少,已得到广泛研究和应用。PSO算法不仅仅是种算法,更是一种学习和思维的创新,体现出学科之间交互所发生的一些突破。它不但是计算机理论上极大的理论创新,而且在哲学上也具有丰富的内涵。对此进行了论述。 相似文献
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The optimal location of a static synchronous compensator (STATCOM) and its coordinated design with power system stabilizers (PSSs) for power system stability improvement are presented in this paper. First, the location of STATCOM to improve transient stability is formulated as an optimization problem and particle swarm optimization (PSO) is employed to search for its optimal location. Then, coordinated design problem of STATCOM-based controller with multiple PSS is formulated as an optimization problem and optimal controller parameters are obtained using PSO. A two-area test system is used to show the effectiveness of the proposed approach for determining the optimal location and controller parameters for power system stability improvement. The nonlinear simulation results show that optimally located STATCOM improves the transient stability and coordinated design of STATCOM-based controller and PSSs improve greatly the system damping. Finally, the coordinated design problem is extended to a four-machine two-area system and the results show that the inter-area and local modes of oscillations are well damped with the proposed PSO-optimized controllers. 相似文献
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阐述了运用粒子群优化人工神经网络建立煤层顶板导水裂隙带高度预测模型的思路与方法。利用粒子群优化神经网络模型的权值和阈值,克服了神经网络容易收敛到局部最小值,以及收敛速度慢的缺点。煤层导水裂隙带高度预测实例表明,该方法不仅能更快地收敛于最优解,且预测精度有明显的提高。 相似文献
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This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of ?50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms. 相似文献
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We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0–1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search. 相似文献
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微粒群算法在工程项目多目标多资源均衡问题中的应用 总被引：2，自引：0，他引：2
微粒群算法(PSO)是继蚁群算法提出之后的又一种新的进化计算技术.介绍了微粒群算法的产生背景、基本算法、算法流程、算法参数,同时基于pso算法的基础上探讨在多目标下如何实现资源更有效地分配,从整体角度出发来考虑单个项目对其他正在进行的项目的影响和实施期间资源的可得性,以最合理的资源配置来满足项目中各个分目标,以达到最好的整体项目效益,为多目标下的多资源配置问题初步建立了模型. 相似文献
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《Journal of The Franklin Institute》2022,359(2):859-869
In this paper, we considered a time-optimal control problem for a new type of linear parameter varying (LPV) system which is obtained through data identification in the process of dealing with actual problems. The addition of non-linear terms is compensation for the method that does not require linear expansion at the equilibrium point. Since the objective function is the terminal time which is an implicit function concerning decision variables, it is a non-standard optimal control problem with uncertain terminal time. To find the global optimal solution to this problem, firstly, the control parameterization method is used to transform it into a nonlinear optimization problem of parameter selection, and then the modifed particle swarm optimization (PSO) algorithm is combined to solve the equivalent nonlinear programming problem. Numerical examples are used to illustrate the effectiveness of the proposed algorithm. 相似文献