一种参数自动寻优的PCNN图像分割算法

利用粒子群优化算法（Particle Swarm Optimization,PSO）具有对参数自动寻优的优势,将PSO和脉冲耦合神经网络（Pulse Couled Neural Network,PCNN）相结合,并以改进的最大类间方差准则函数为适应度函数,提出了一种能进行参数自动寻优的PCNN图像自动分割算法。实验仿真结果验证了该方法的有效性,即不仅可以正确地实现图像分割,而且PCNN的参数可以自动设置省去了人工实验的麻烦,同时分割速度也有所提高。     

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PSO信息传递模式的混合算法

粒子群优化算法（Particle Swarm Optimization,PSO）是一种基于群智能（Swarm Intelligence）的随机优化计算技术。PSO和遗传算法这两种算法相比较,PSO收敛快速准确,但编码形式单一,局限于解决实优化问题,而遗传算法编码形式灵活,解决问题广泛,但执行效率低于PS00。将粒子群算法的信息传递模式与遗传算法的编码和遗传操作相结合,提出一种混合算法。并推导了两个算法之间的密切联系。并通过组合优化和函数优化的基准测试集对算法进行测试,试验结果表明,该算法在收敛精度和速度优于传统遗传算法。同时,也观察到该算法取得了与粒子群算法一致的收敛现象。     

基于改进粒子群优化算法的AGV全局路径规划

分析了用人工神经网络模型描述环境时,采用Sigmoid函数作为神经网络作用函数的不足之处,提出采用双曲正切函数作为神经网络的作用函数,使网络更有利于路径优化算法的寻优计算。粒子群优化（Particle Swarm Optimization,PSO）算法具有收敛速度快,需要调节的参数少等优点,但优化过程中容易发生“早熟”收敛,使优化陷入局部极小值。通过引入模拟退火算法、“交叉算子”和“变异算子”,提出了一种新的改进粒子群优化算法（Improved Particle Swarm Optimization,IPSO）来解决AGV全局路径规划问题。仿真结果表明,IPSO具有很强的全局寻优能力,并且收敛速度比PSO快,能够为AGV规划出理想的路径。     

基因变异的群智能优化算法研究

粒子群优化（Particle Swarm Optimization,PSO）是一种重要的群智能（Swarm Intelligence,SI）方法。早期收敛和较低的局部搜索能力是PSO的不足。提出一种新颖的基因变异PSO（Gene Mutation PSO,GMPSO）,依据概率使粒子的分量发生变异,并做了大量的实验。研究和实验的结果表明,该方法可显著改变PSO的性能,在理论上是可靠的,技术上是可行的。     

关于PSO方法中粒子运行轨迹的修正

粒子群优化方法（PSO Particle Swarm Optimization）由Kennedy和Eberhart于1995年提出,基于群体智能行为的演化计算方法,并广泛应用于各类优化问题.在一些研究中,对PSO的粒子收敛性及粒子运行轨迹进行了分析,有一定理论价值和指导意义,本文针对一些分析过程中存在的问题进行了讨论,并对相关结论进行了修正.     

自适应简化粒子群优化算法及其应用

针对粒子群优化算法（Particle Swarm Optimization，PSO）寻优速度慢、收敛精度不高且搜索结果波动性较大的缺点，提出了一种自适应简化粒子群优化算法（Self-Adjusted Simplified Particle Swarm Optimization，SASPSO）。在每次迭代过程中，粒子只受全局最优解影响，且加入按一定规律分布的锁定因子，令粒子受影响的程度有规律性。同时，利用锁定因子和当前粒子位置令惯性权重自适应配置，更有效地利用惯性权重对粒子群优化算法的影响。引入4种近期提出的改进粒子群算法同时搜索不同维度时的18个基准函数，与SASPSO的搜索结果对比，并使用T-test进行差异性分析。为了进一步分析算法性能，统计5个改进算法搜索100维函数达到期望值时的成功率与平均迭代次数。实验结果证明，SASPSO在无约束问题寻优中的收敛速度、寻优精度有了明显提升，且搜索结果异常值较少，波动性弱。将SASPSO应用于机床主轴结构参数优化问题，结果显示SASPSO优化性能更好。     

基于距离的k最优粒子群优化算法

传统的粒子群优化算法（Particle Swarm Optimization,PSO）只考虑了最优粒子对整个进化过程的引导作用且在一次迭代中所有粒子采用相同的惯性权值。为了体现各粒子相对于已知最优解的差异,提出了一种基于距离度量的自适应（k,l）PSO算法。（k,l）PSO算法采用轮盘赌策略在k个最优的粒子中选择一个粒子作为全局最优粒子参与粒子的速度更新,同时,根据粒子间的平均距离l确定粒子与选中的最优粒子的距离,自适应调整粒子的惯性权值。通过基准测试函数对算法进行了实验,实验验证了（k,l）PSO算法的有效性。     

基于动态概率变异的Cauchy粒子群优化

介绍了标准粒子群优化（SPSO）算法,在两种粒子群改进算法Gaussian Swarm和Fuzzy PSO的基础上提出了Cauchy粒子群优化（CPSO）算法,并将遗传算法中的变异操作引入粒子群优化,形成了动态概率变异Cauchy粒子群优化（DMCPSO）算法。用3个基准函数进行实验,结果表明,DMCPSO算法性能优于SPSO和CPSO算法。     

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。     

基于粒子群优化算法的约束布局优化

布局优化是NP难问题，也是复杂的非线性约束优化问题．针对这个问题，将新的基于粒子群优化的方法应用于布局参数的优化，提出了适合粒子群优化的约束处理，并通过与直接搜索算法的混合，加强了算法在局部区域的搜索能力．通过实例将该算法与乘子法以及基于遗传算法的布局优化方法进行了比较．仿真结果表明，该算法可以提高布局优化问题解的质量，同时降低计算费用．     

Parallel Implementations of the Cooperative Particle Swarm Optimization on Many-core and Multi-core Architectures

Particle swarm optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. PSO is stochastic yet very robust. Nevertheless, real-world optimizations require a high computational effort to converge to a good solution for the problem. In general, parallel PSO implementations provide good performance. However, this depends heavily on the parallelization strategy used as well as the number and characteristics of the exploited processors. In this paper, we propose a cooperative strategy, which consists of subdividing an optimization problem into many simpler sub-problems. Each of these focuses on a distinct subset of the problem dimensions. The optimization work for all the selected sub-problems is done in parallel. We map the work onto four different parallel high-performance multiprocessors, which are based on multi- and many-core architectures. The performance of the strategy thus implemented is evaluated for four well known benchmark functions with high-dimension and different complexity. The obtained speedups are compared to that yielded by a serial PSO implementation.     

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.     

求解作业车间调度问题的微粒群遗传退火算法

标准微粒群算法（PSO）通常被用于求解连续优化的问题,很少被用于离散问题的优化求解,如作业车间调度问题（JSP）。因此,针对PSO算法易早熟、收敛慢等缺点提出一种求解作业车间调度问题（JSP）的混合微粒群算法。算法将微粒群算法、遗传算法（GA）、模拟退火（SA）算法相结合,既增强了算法的局部搜索能力,降低了算法对参数的依赖,同时改善了PSO算法和GA算法易早熟的缺点。对经典JSP问题的仿真实验表明：与标准微粒群算法相比,该算法不仅能有效避免算法中的早熟问题,并且算法的全局收敛性得到了显著提高。     

Robust state estimator design for uncertain linear systems using optimization techniques

The estimation of state variables of dynamic systems in noisy environments has been an active research field in recent decades. In this way, Kalman filtering approach may not be robust in the presence of modeling uncertainties. So, several methods have been proposed to design robust estimators for the systems with uncertain parameters. In this paper, an optimized filter is proposed for this problem considering an uncertain discrete-time linear system. After converting the subject to an optimization problem, three algorithms are used for optimizing the state estimator parameters: particle swarm optimization (PSO) algorithm, modified genetic algorithm (MGA) and learning automata (LA). Experimental results show that, in comparison with the standard Kalman filter and some related researches, using the proposed optimization methods results in robust performance in the presence of uncertainties. However, MGA-based estimation method shows better performance in the range of uncertain parameter than other optimization methods.     

Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects

This paper presents a chaotic self-adaptive particle swarm optimization algorithm (CSAPSO) to solve dynamic economic dispatch problem (DED) with value-point effects. The proposed algorithm takes PSO as the main evolution method. The velocity, a sensitive parameter of PSO, is adjusted dynamically to increase the precision of PSO. To overcome the drawback of premature in PSO, chaotic local search is imported into proposed algorithm. Moreover, a new strategy is proposed to handle the various constraints of DED problem in this paper, the results solved by proposed strategy can satisfy the constraints of DED problem well. Finally, the high feasibility and effectiveness of proposed CSAPSO algorithm is validated by three test systems consisting of 10 and extended 30 generators while compared with the experimental results calculated by the other methods reported in this literature.     

基于IPSO-LS-SVM的异步电动机转子故障诊断

针对异步电动机转子的故障诊断问题,为了提高诊断精度和诊断效率,提出基于混合核函数的最小二乘支持向量机与改进的粒子群算法相结合(IPSO-LS-SVM)的故障诊断方法.首先对PSO的惯性权值策略进行研究,给出一种非线性递减惯性权值策略,然后利用改进后的PSO优化基于混合核函数的LS-SVM,最后,应用改进算法完成转子的故障诊断.结果表明,改进算法通过较少的迭代次数即寻找到最优参数,克服了陷入局部极小值的缺陷,诊断效率和诊断精度都得到了提升.     

Real-time particle swarm optimization based current harmonic cancellation

As a powerful optimization algorithm, particle swarm optimization (PSO) has been widely applied to power system researches. However, most existing applications of PSO can only be implemented offline. The difficulties of online implementation mainly come from the unavoidable lengthy simulation time to evaluate a candidate solution. Recently, PSO was implemented online that can identify parameters in a motor control systems. In this paper, the real-time PSO (RT-PSO) based identification technique is applied to cancel current harmonics in power systems. By transforming the identification problem to optimization problem, RT-PSO can simultaneously identify four parameters associated with fundamental current from measurement. In this way, there is no need to identify the fundamental frequency separately or construct fundamental signal from identified harmonic information. The identification algorithm can be applied to three-phases independently, even for unbalanced system or single-phase system. The identified fundamental signal is then used as the reference for current harmonics cancellation. The RT-PSO based harmonic cancellation is realized with an active filter and used to compensate harmonic current created by a nonlinear load. Simulation results demonstrate that the RT-PSO algorithm can provide accurate identification of the fundamental current which in turn will result in good harmonic cancellation performance. As a capable online optimization technique, RT-PSO can be extensively applied to many optimization and control problems.     

STEREO CAMERA CALIBRATION USING PARTICLE SWARM OPTIMIZATION

Camera calibration is an essential issue in many computer vision tasks in which quantitative information of a scene is to be derived from its images. It is concerned with the determination of a set of parameters from the given images. In literature, it has been modeled as a nonlinear global optimization problem and has been solved using various optimization techniques. In this article, a recently developed variant of a very popular global optimization technique—the particle swarm optimization (PSO) algorithm—has been used for solving this problem for a stereo camera system modeled by pin-hole camera model. Extensive experiments have been performed on synthetic data to test the applicability of the technique to this problem. The simulation results, which have been compared with those obtained by a real coded genetic algorithm (RCGA) in literature, show that the proposed PSO performs a bit better than RCGA in terms of computational effort.     

A parameter selection strategy for particle swarm optimization based on particle positions

In this study, we found that engineering experience can be used to determine the parameters of an optimization algorithm. We came to this conclusion by analyzing the dynamic characteristics of PSO through a large number of experiments. We constructed a relationship between the dynamic process of particle swarm optimization and the transition process of a control system. A novel parameter strategy for PSO was proven in this paper using the overshoot and the peak time of a transition process. This strategy not only provides a series of flexible parameters for PSO but it also provides a new way to analyze particle trajectories that incorporates engineering practices. In order to validate the new strategy, we compared it with published results from three previous reports, which are consistent or approximately consistent with our new strategy, using a suite of well-known benchmark optimization functions. The experimental results show that the proposed strategy is effective and easy to implement. Moreover, the new strategy was applied to equally spaced linear array synthesis examples and compared with other optimization methods. Experimental results show that it performed well in pattern synthesis.