带组织的粒子群优化同步并行算法

提出带组织的粒子群优化同步并行算法.粒子群优化算法是一种基于群体智能的演化算法,具有良好的优化性能.但由于群体的迅速收敛和多样性低,导致算法早熟收敛.带组织的粒子群优化同步并行算法虽然克服了早熟收敛问题,但无形中却增加了计算时间.结合已有的并行计算技术,构造出了该方法的同步并行计算算法,仿真试验证明并行算法具有更快的收敛速度.     

Multi swarm bare bones particle swarm optimization with distribution adaption

Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. However, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense impact on the performance of the bare bones PSO. As a result, this paper investigates the use of different methods for estimating the probability distributions used in the update rule. Four methods or strategies are developed that are using Gaussian or multivariate Gaussian distributions. The choice of an appropriate updating strategy for each particle greatly depends on the characteristics of the fitness landscape that surrounds the swarm. To deal with issue, the cellular learning automata model is incorporated with the proposed bare bones PSO, which is able to adaptively learn suitable updating strategies for the particles. Through the interactions among its elements and the learning capabilities of its learning automata, cellular learning automata gradually learns to select the best updating rules for the particles based on their surrounding fitness landscape. This paper also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies. The proposed method is compared with some other well-known particle swarm approaches. The results indicate the superiority of the proposed approach in terms of the accuracy of the achieved results and the speed in finding appropriate solutions.     

粒子群优化算法的收敛性分析及其混沌改进算法

分析了粒子群优化算法的收敛性,指出它在满足收敛性的前提下种群多样性趋于减小,粒子将会因速度降低而失去继续搜索可行解的能力;提出混沌粒子群优化算法,该算法在满足收敛性的条件下利用混沌特性提高种群的多样性和粒子搜索的遍历性,将混沌状态引入到优化变量使粒子获得持续搜索的能力.实验结果表明混沌粒子群优化算法是有效的,与粒子群优化算法、遗传算法、模拟退火相比,特别是针对高维、多模态函数优化问题取得了明显改善.     

基于群能量恒定的粒子群优化算法

针对标准粒子群优化(PSO)算法在寻优过程中容易出现早熟的情况,提出一种群能量恒定的粒子群优化(SEC-PSO)算法.算法根据粒子内能进行动态分群,对较优群体采取引入最差粒子的速度更新策略,对较差群体采取带有惩罚机制的速度更新策略,由其分担由于较优群体速度降低而产生的整群能量损失,从而有效地避免了PSO算法的早熟.典型优化问题的仿真结果表明,该算法具有较强的全局搜索能力和较快的收敛速度,优化性能得到显著的提高.     

A modified particle swarm optimization via particle visual modeling analysis

A particle is treated as a whole individual in all researches on particle swarm optimization (PSO) currently, these are not concerned with the information of every particle’s dimensional vector. A visual modeling method describing particle’s dimensional vector behavior is presented in this paper. Based on the analysis of visual modeling, the reason for premature convergence and diversity loss in PSO is explained, and a new modified algorithm is proposed to ensure the rational flight of every particle’s dimensional component. Meanwhile, two parameters of particle-distribution-degree and particle-dimension-distance are introduced into the proposed algorithm in order to avoid premature convergence. Simulation results of the new PSO algorithm show that it has a better ability of finding the global optimum, and still keeps a rapid convergence as with the standard PSO.     

Particle Swarm Optimization with Chaos-based Initialization for Numerical Optimization

Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other hand, Gaussian mutation as well as the local re-initialization strategy based on the maximal focus distance is exploited to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space. In addition, an auxiliary velocity-position update strategy is exclusively used for the global best particle, which can effectively guarantee the convergence of the proposed particle swarm optimization. Extensive experiments on eight well-known benchmark functions with different dimensions demonstrate that the proposed PSO is superior or highly competitive to several state-of-the-art PSO variants in dealing with complex multimodal problems.     

一种高速收敛粒子群优化算法

针对粒子群优化算法早熟问题,提出一种克服早熟的高速收敛粒子群算法．该算法首先采用混沌序列初始化粒子位置,以增强搜索多样性;其次,在算法中嵌入有效判断早熟停滞的方法,一旦检索到早熟迹象,便随机地选择最优解任意一维的分量值,用一个随机值取代它,以扰乱粒子的当前搜索轨迹,使其跳出局部最优．大量仿真实验表明,大多数连续函数的寻优过程只需用几个粒子、迭代几十次便能完成,可实现全局寻优过程的高速收敛．     

基于混沌和差分进化的混合粒子群优化算法

研究粒子群算法优化问题,由于标准粒子群优化算法(PSO)在高维复杂函数优化中易早收敛,影响全系统优化。为改进的混合粒子群优化算法,提出了一种基于混沌和差分进化的混合粒子群优化算法(CDEHPSO)。把基于Logistic映射的混沌序列引入到种群初始化操作中。在算法进化过程中,通过一种粒子早熟判断机制,在基本粒子群优化算法中引入了差分变异、交叉和选择操作,对早熟粒子个体进行差分进化操作,从而维持了种群的多样性并有效避免了算法陷入局部最优。仿真结果表明,相比于粒子群优化算法和差分进化算法(DE),CDEHPSO算法具有收敛速度快、搜索能力强的优点。     

混合拓扑结构的粒子群算法及其在测试数据生成中的应用研究

粒子群算法(PSO)的拓扑结构是影响算法性能的关键因素,为了从根源上避免粒子群算法易陷入局部极值及早熟收敛等问题,提出一种混合拓扑结构的粒子群优化算法(MPSO)并将其应用于软件结构测试数据的自动生成中。通过不同邻域拓扑结构对算法性能影响的分析,采用一种全局寻优和局部寻优相结合的混合粒子群优化算法。通过观察粒子群的多样性反馈信息,对每一代种群粒子以进化时选择全局拓扑结构模型(GPSO)或局部拓扑结构模型(LPSO)的方法进行。实验结果表明,MPSO使得种群的多样性得到保证,避免了粒子群陷入局部极值,提高了算法的收敛速度。     

一种自适应柯西变异的反向学习粒子群优化算法

针对传统粒子群优化算法易出现早熟的问题,提出了一种自适应变异的反向学习粒子群优化算法。该算法在一般性反向学习方法的基础上,提出了自适应柯西变异策略(ACM)。采用一般性反向学习策略生成反向解,可扩大搜索空间,增强算法的全局勘探能力。为避免粒子陷入局部最优解而导致搜索停滞现象的发生,采用ACM策略对当前最优粒子进行扰动,自适应地获取变异点,在有效提高算法局部开采能力的同时,使算法能更加平稳快速地收敛到全局最优解。为进一步平衡算法的全局搜索与局部探测能力,采用非线性的自适应惯性权值。将算法在14个测试函数上与多种基于反向学习策略的PSO算法进行对比,实验结果表明提出的算法在解的精度以及收敛速度上得到了大幅度的提高。     

全变异粒子群优化算法

针对粒子群优化算法容易早熟、收敛精度低等缺点,通过采用全变异策略、最大搜索速度自适应调整等策略得到了一种全变异粒子群优化算法,其中的全变异策略是在陷入早熟的条件下全体粒子参加变异,并且当把粒子看成染色体时,每一个基因等概率地参加变异,可以克服算法的早熟而继续优化,提高了算法的收敛精度。对Shubert函数进行实验的结果表明了算法的有效性。     

Dispersed particle swarm optimization

In particle swarm optimization (PSO) literatures, the published social coefficient settings are all centralized control manner aiming to increase the search density around the swarm memory. However, few concerns the useful information inside the particles' memories. Thus, to improve the convergence speed, we propose a new setting about social coefficient by introducing an explicit selection pressure, in which each particle decides its search direction toward the personal memory or swarm memory. Due to different adaptation, this setting adopts a dispersed manner associated with its adaptive ability. Furthermore, a mutation strategy is designed to avoid premature convergence. Simulation results show the proposed strategy is effective and efficient.     

An improved self-adaptive PSO technique for short-term hydrothermal scheduling

This paper presents an improved self-adaptive particle swarm optimization algorithm (ISAPSO) to solve hydrothermal scheduling (HS) problem. To overcome the premature convergence of particle swarm optimization (PSO), the evolution direction of each particle is redirected dynamically by adjusting the two sensitive parameters of PSO in the evolution process. Moreover, a new strategy is proposed to handle the various constraints of HS problem in this paper. The results solved by this proposed strategy can strictly satisfy the constraints of HS problem. Finally, the feasibility and effectiveness of proposed ISAPSO algorithm is validated by a test system containing four hydro plants and an equivalent thermal plant. The results demonstrate that the proposed ISAPSO can get a better solution in both robustness and accuracy while compared with the other methods reported in this literature.     

基于邻域混沌PSO算法的目标分配优化方法

针对基本粒子群算法在求解火力打击体系目标分配问题时易陷入局部极值、计算精度差的局限性,提出了一种基于混沌粒子群算法（ChaosParticleSwarmOptimization,CPSO）的目标分配优化方法。在综合考虑整体毁伤效能、打击匹配度和风险概率的基础上,分析了目标分配问题的数学模型,设计了相应的粒子编码方法、更新策略和有效性修订方法,提出一种在种群最优粒子邻域内进行混沌搜索的改进策略。仿真结果表明,所提CPSO算法的性能明显优于基本粒子群算法和变异粒子群算法。     

一种改进的粒子群优化算法及其仿真

粒子群优化算法(particle swarm optimization,PSO)是一种新兴的优化技术,其思想来源于人工生命和演化计算理论。PSO算法具有简单、易实现、可调参数少等特点,在很多领域得到了广泛应用。但PSO算法存在早熟收敛问题。为了克服粒子群优化算法的早熟收敛问题,提出了一种旨在保持种群多样性的改进PSO(IPSO)算法,以提高PSO算法摆脱局部极小点的能力。通过对3种Benchmark函数的测试,结果表明IPSO算法不仅具有较快的收敛速度、有效的全局收敛性能,而且还具有良好的稳定性。     

基于混合粒子群算法的锌电解过程能耗优化

针对锌电解过程能耗过高的情况,研究其能耗优化问题.根据电力部门实行的分时计价政策,建立以全天锌电解过程电能消耗和总用电费用为目标的锌电解过程多目标优化模型.提出一种带加速度调整的粒子群优化算法,当粒子陷入局部最优时,通过加速度策略增强种群速度,使算法获得持续搜索的能力,有效克服早熟收敛;并和Powell算法相结合构成新的混合粒子群算法,将粒子群算法的全局搜索能力与Powell算法的局部寻优能力有机结合起来.最后将该混合粒子群算法应用于所建优化模型的求解,获得优化生产方案.仿真结果证明了该算法的有效性.工业应用效果表明,按所得优化方案组织生产降低了电能消耗,减少了用电费用.     

一种基于多粒子群协同进化的高光谱图像波段选择与分类方法

针对高光谱图像分类过程中数据波段多以及信息冗余量大引起的处理速度慢及Hughes现象等问题,提出了一种基于多粒子协同进化算法进行高光谱图像自动波段选择与分类的方法:使用多粒子群协同进化算法搜索特征子集,对粒子群优化算法进行改进,定义新的位置和速度的更新策略,并以支持向量机为分类器,同时对特征子集和SVM核函数参数进行优化。在协同搜索过程中,引入遗传算法改善粒子群优化的"早熟"收敛问题,构建了一种新的MPSO-SVM(Multiple particle swarm optimization-SVM)分类模型。对高光谱遥感图像的实验结果表明:MPSO-SVM方法不仅能有效地压缩光谱的特征维数,得到最佳的波段组合,还能得到最优的SVM参数,达到较好的分类效果,提高分类精度。     

嵌入层叠混沌策略的随机粒子群算法

鉴于求解复杂问题时粒子群优化算法易出现早熟收敛的问题,通过引入轨迹扰动因子,提出随机粒子群进化迭代方程.该方程在统计行为中保证粒子向特定的收敛中心逼近,但对“旧址”的依赖性呈现出随机特性,从而使粒子群的快速跳转和迁移成为可能,避免过早落入局部陷阱.同时该进化方程还利用层叠混沌策略和对称极值扰动策略进一步增强算法的局部收敛性和全局搜索性.实验表明,由上述进化方程和改进策略构成的随机混沌粒子群算法具有鲁棒性较强、收敛速度较快和精度较高等优势,性能优于其他同源粒子群算法.     

一种克服粒子群早熟的混合优化算法

针对粒子群优化算法在寻优时容易出现早熟现象,提出在粒子群收敛停滞时,从种群中随机选择粒子进行共轭梯度法计算,通过引入共轭梯度算法计算的信息来影响粒子速度的更新,以保持群体的活性,从而打破群体信息陷入局部最优的状况.不同于传统的粒子群算法,该算法有机地结合了粒子群的全局搜索能力和共轭梯度法的强大局部搜索能力,从而在一定程度上有效地克服了粒子群早熟的缺点.仿真计算结果表明,该改进粒子群的方法对于不同维数的非线性函数具有很好的寻优效果.     

一种基于栅格的动态粒子数微粒群算法

微粒群算法的全局搜索性能容易受到局部极值点的影响,对此,提出一种基于栅格的动态粒子数微粒群算法(GB-DPPPSO).通过设计栅格信息更新策略、粒子产生策略和粒子消灭策略,可以根据种群搜索情况动态控制粒子数变化,以保持种群多样性,提高全局搜索性能,通过对4个典型数学验证函数的仿真实验,表明了该算法相对于DPPPSO)在全局搜索成功率和搜索效率两方面均有明显改进.