一种基于量子行为的改进粒子群算法

研究粒子群优化算法（PSO）的收敛速度,以提高该算法性能是PSO的一个重要而且有意义的研究。Jun Sun 等人通过对PSO系统下的单个个体在量子多维空间的运动及其收敛性的分析,提出了具有函数形式的粒子群算法（Quantum Delta-Potential-Well-based PSO）。在此基础上进行了改进,用粒子的速度来产生一个随机数引导粒子向最优解快速靠拢,并对速度的处理采取了新的策略。仿真结果表明：该改进算法对收敛速度有非常好的改善,而且稳定性也较好。     

基于量子粒子群算法的收敛性研究

对基于量子行为的粒子群算法(QPSO)的收敛性进行分析.QPSO算法不仅参数个数少,随机性强,并且能覆盖所有解空间,保证算法的全局收敛性.通过四个经典的基准函数对算法进行测试,将QPSO算法与PSO算法进行深入比较.通过实验结果表明.QPSO算法在收敛性能上大大优于PSO算法.     

基于差分方程的PSO算法粒子运动轨迹分析

针对PSO是一个动态离散过程的特点，文中通过差分方程及Z变换对PSO算法中粒子运动轨迹的稳定性做深入的研究，讨论了pBest、gBest以及随机性对粒子运动过程的影响，分析了粒子运动稳定性与算法收敛性之间的关系，并给出了选择PSO算法参数的理论指导公式和条件，用以指导平衡算法的exploration能力和exploitation能力，有助于实际应用中PSO算法参数的选择和调整．     

一种改进的粒子群优化算法

粒子群优化算法（PSO）是一种生物进化技术。依据粒子间的相互影响发现搜索空间中的最优解。通过分析基本PSO算法的进化方程,研究了一种具有更好收敛速度和全局收敛性的改进PSO算法。5个典型测试函数的仿真实验表明该改进算法是行之有效的。     

粒子群算法研究与展望

介绍了基本粒子群算法,归纳了粒子群算法的研究现状和改进,包括:增加惯性因子的改进;基于收敛性分析的改进;导入其他演化算法思想的改进;建立非数值问题模型的改进.简要分析了PSO算法的应用.最后对PSO的研究现状做出总结和展望,提出未来的几个研究热点.     

粒子群优化算法研究进展

粒子群优化(PSO)算法作为一种仿生进化算法,是受到自然界生物群体行为机制的启发而提出的.本文首先介绍PSO算法的基本原理和工作机制.然后着重就PSO算法的理论和应用研究现状进行综述,包括PSO算法的改进、PSO算法的参数设置、PSO算法的收敛性、PSO算法与其它算法的融合以及PSO算法在优化领域的典型应用,并进一步分析它们的研究重点和发展方向.最后是关于PSO算法面临的问题和研究展望,提出PSO算法研究中值得探讨的一些课题.     

具有分工策略的量子粒子群优化算法

基于量子行为的粒子群优化算法是一种随机的全局优化搜索新方法.介绍了PSO算法和QPSO算法,在对QPSO算法和基于分工策略的PSO算法分析的基础上,提出了基于分工策略的QPSO算法,然后对新算法进行实验.实验结果表明,新算法在收敛性和取得最优值方面优于基于分工策略的PSO算法.     

粒子群优化算法综述

为了进一步推广应用粒子群优化算法(PSO)并为深入研究该算法提供相关资料,在分析PSO基本原理和机制的基础上,从参数设置.收敛性、拓扑结构及与其它算法混合等方面对其发展历程和研究现状进行深入调查,论述了该算法的各种改进技术,并阐述了PSO在连续领域和离散领域的应用成果,最后对该算法未来发展趋势做出了展望.     

基于PSO和变异模拟退火的QoS单播路由算法

为了研发更高性能的QoS单播路由算法,提出变异退火粒子群优化（MSAPSO）算法。MSAPSO算法中使用一种新的。算子,将粒子群优化（PSO）的迭代公式简化成一个公式。通过设计变异退火算子,将遗传算法的变异操作和模拟退火的Meuopofis概率接受准则融入PSO,以改善粒子群的多样性和算法的收敛性。仿真结果表明MSAPSO在搜索成功率和收敛性上优于纯PSO算法和蚁群算法。     

一种改进的RBF神经网络学习算法

提出一种基于减聚类、K-means算法及改进的粒子群优化(PSO)算法的径向基函数(RBF)神经网络混合学习算法. 该算法首先使用减聚类确定隐层节点数和K-means初始聚类中心; 然后通过K-means算法求取RBF网络所有参数, 作为PSO的初始粒子群; 为了提高PSO算法的收敛性和稳定性, 对基本PSO算法进行了优化改进, 最后使用改进的PSO算法训练RBF神经网络中的所有参数. 对IRIS数据集分类识别的仿真结果表明, 改进的混合算法具有更高的分类准确率和更好的稳定性.     

复杂约束条件下的混合粒子群优化算法*

针对具有复杂约束条件的优化问题,提出了一种混合粒子群算法。该混合算法在将标准粒子群算法与线性搜索法有机结合的基础上,依次对粒子的每一维变量进行适当变化并同时判断其变化的效果。最后进行了数值实验,其结果表明,所提出的混合粒子群算法对于具有复杂有约束条件的优化问题有较好的优化效果。     

群核进化粒子群优化方法

粒子群优化方法（PSO Particle Swarm Optimization）是由Kennedy和Eberhart于1995年提出的进化计算技术,并成功应用于各类优化问题。其基本思想源于对鸟群捕食等群体行为的研究。本文对标准PSO方法进行了分析,给出了“群核”（Swarm-Core）的概念,并在此基础上,提出了群核进化粒子群优化方法（Swarm-Core Evolutionary Particle Swarm Optimization,SCEPSO）,同时把该方法与其它版本PSO方法进行了比较。试验结果表明：在相同环境下,SCEPSO方法能较好地克服传统PSO方法中的不足,测试结果较其它几个版本的PSO方法有很大提高,是非常有效的。     

Particle swarm optimization with query-based learning for multi-objective power contract problem

Particle swarm optimization (PSO) is one of the most important research topics on swarm intelligence. Existing PSO techniques, however, still contain some significant disadvantages. In this paper, we present a new QBL-PSO algorithm that uses QBL (query-based learning) to improve both the exploratory and exploitable capabilities of PSO. Here, we apply a QBL method proposed in our previous research to PSO, and then test this new algorithm on a real case study on problems of power conservation. Our algorithm not only broadens the search diversity of PSO, but also improves its precision. Conventional PSO often snag on local solutions when performing queries, instead of finding better global solutions. To resolve this limitation, when particles converge in nature, we direct some of them into an “ambiguous solution space” defined by our algorithm. This paper introduces two ways to invoke this QBL algorithm. Our experimental results confirm that the proposed method attains better convergence to the global best solution. Finally, we present a new PSO model for solving multi-objective power conservation problems. Overall, this model successfully reduces power consumption, and to our knowledge, this paper represents the first attempt within the literature to apply the QBL concept to PSO.     

r-SVR的PSO求解方法

针对r接近1时牛顿法不可求解r-SVR的问题,提出了r-SVR的PSO求解方法,推导出了PSO求解r-SVR的适应函数,给出了PSO求解r-SVR的算法和实验结果。结果表明,当r接近1时,牛顿法求解r-SVR失效,而PSO求解r-SVR的方法是有效的。     

A probability matrix based particle swarm optimization for the capacitated vehicle routing problem

Particle swam optimization (PSO) is a relatively new metaheuristic that has recently drawn much attention from researchers in various optimization areas. However, application of PSO for the capacitated vehicle routing problem (CVRP) is very limited. This paper proposes a simple PSO approach for solving the CVRP. The proposed PSO approach uses a probability matrix as the main device for particle encoding and decoding. While existing research used the PSO solely for assignment of customers to routes and used other algorithms to sequence customers within the routes, the proposed approach applies the PSO approach to both simultaneously. The computational results show the effectiveness of the proposed PSO approach compared to the previous approaches.     

A novel stability-based adaptive inertia weight for particle swarm optimization

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the PSO algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of PSO are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the PSO performance in terms of the solution quality as well as convergence speed in static and dynamic environments.     

改进的粒子群算法在化工过程优化中的应用

在现有自适应粒子群优化算法的研究基础上本文引入1种反弹机制(Rebound Mechanism),提出了1种改进的粒子群算法——反弹自适应粒子群优化算法。RAPSO能在搜索过程中充分利用粒子的飞行速度和方向等信息(下文称为动量信息),维持粒子的多样性以提升算法的搜索性能。通过比较,本文提出的RAPSO在一定程度上改进了现有的自适应粒子群算法的优化性能。运用RAPSO对催化裂化装置进行优化试验,其结果表明无论在单变量优化还是在多变量优化中,该装置的转化率都得到了一定程度的提高。     

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality. Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)     

基于聚度的PSO参数分析

对PSO方法中粒子运行规律给出较为完整的分析,考察随机性对粒子运动过程的影响,提出聚度的概念,并 通过粒子的聚度考察粒子在实际运行条件下的分布情况,给出更加具体的参数设置区间,提出一种粒子在运动过程中 的速度补偿策略,对于一些参数设置可以通过该策略提高搜索性能,该策略对实际应用中选择和调整PSO算法参数 有较强的指导意义。     

On the use of particle swarm optimization for adaptive resource allocation in orthogonal frequency division multiple access systems with proportional rate constraints

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling algorithm. In this paper we have proposed the use of a customized particle swarm optimization (PSO) aided algorithm to allocate the subchannels. The PSO algorithm is population-based: a set of potential solutions evolves to approach a near-optimal solution for the problem under study. The customized algorithm works for discrete particle positions unlike the classical PSO algorithm which is valid for only continuous particle positions. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.