Hardware/software co-design for particle swarm optimization algorithm

This paper presents a hardware/software (HW/SW) co-design approach using SOPC technique and pipeline design method to improve design flexibility and execution performance of particle swarm optimization (PSO) for embedded applications. Based on modular design architecture, a Particle Updating Accelerator module via hardware implementation for updating velocity and position of particles and a Fitness Evaluation module implemented either on a soft-cored processor or Field Programmable Gate Array (FPGA) for evaluating the objective functions are respectively designed to work closely together to carry out the evolution process at different design stages. Thanks to the design flexibility, the proposed approach can tackle various optimization problems of embedded applications without the need for hardware redesign. To further improve the execution performance of the PSO, a hardware random number generator (RNG) is also designed in this paper in addition to a particle re-initialization scheme to promote exploration search during the optimization process. Experimental results have demonstrated that the proposed HW/SW co-design approach for PSO algorithms has good efficiency for obtaining high-quality solutions for embedded applications.     

遗传/粒子群混合算法在飞剪机结构优化中的应用

飞剪机结构参数设计需满足若干技术性能要求才能保证剪切质量。飞剪机结构参数优化设计问题要满足多个非线性约束要求,同时需优化多个目标函数,提出遗传算法/粒子群混合算法用于曲柄连杆式飞剪机结构参数优化设计,结合各自算法的优势,在算法运行初期利用遗传算法的全局搜索能力进行优化搜索,在算法运行后期利用粒子群较强的局部搜索能力进行搜索,综合考虑多个目标函数和约束条件,通过实例计算表明,该混合方法可以稳定、有效的获取到满意的优化设计结果。     

基于锦标赛选择遗传算法的随机微粒群算法

以保证全局收敛的随机微粒群算法SPSO为基础。提出了一种改进的随机微粒群算法-GAT-SPSO。该方法是在SPSO的进化过程中．以锦标赛选择机制下的遗传算法所产生的最优个体来代替SPSO中停止的微粒,参与下一代的群体进化。通过时三个多峰的测试函数进行仿真,其结果表明：在搜索空间维数相同的情况下,GAT-SPSO的收敛率厦收敛速度均大大优于SPSO。     

融合粒子群优化和遗传算法的基因调控网络构建

MicroRNA（miRNA）是一类大小为21~25 nt的内源性非编码小核糖核酸（RNA）,通过与mRNA的3’-UTR互补结合,导致mRNA降解或翻译抑制来调控编码基因的表达。为了提高构建基因调控网络的准确度,提出一种基于粗糙集、融合粒子群（PSO）和遗传算法（GA）的基因调控网络构建方法（PSO-GA-RS）。该方法首先通过对序列信息进行特征提取;然后采用粗糙集的依赖度作为适应度函数,融合粒子群和遗传算法选出较优的特征子集;最后使用支持向量机（SVM）建立模型,预测未知的调控关系。在拟南芥数据集上进行实验,相比基于粗糙集和粒子群优化的特征选择方法和Rosetta算法,所提方法的预测准确率、F值和受试者工作特征（ROC）曲线面积最多能提高5%,在水稻数据集上最多能提高8%。实验结果表明所提方法能够比较准确地预测miRNA和靶基因之间的调控关系。     

改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法

针对支持向量机(SVM)中特征选择和参数优化对分类精度有较大影响,提出了一种改进的基于粒子群优化(PSO)的SVM特征选择和参数联合优化算法(GPSO-SVM),使算法在提高分类精度的同时选取尽可能少的特征数目。为了解决传统粒子群算法在进行优化时易出现陷入局部最优和早熟的问题,该算法在PSO中引入遗传算法(GA)中的交叉变异算子,使粒子在每次迭代更新后进行交叉变异操作来避免这一问题。该算法通过粒子之间的不相关性指数来决定粒子之间的交叉配对,由粒子适应度值的大小决定其变异概率的大小,由此产生新的粒子进入到群体中。这样使得粒子跳出当前搜索到的局部最优位置,提高了群体的多样性,在全局范围内寻找更优值。在不同数据集上进行实验,与基于PSO和GA的特征选择和SVM参数联合优化算法相比,GPSO-SVM的分类精度平均提高了2%~3%,选择的特征数目减少了3%~15%。实验结果表明,所提算法的特征选择和参数优化效果更好。     

A new gradient based particle swarm optimization algorithm for accurate computation of global minimum

Stochastic optimization algorithms like genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms perform global optimization but waste computational effort by doing a random search. On the other hand deterministic algorithms like gradient descent converge rapidly but may get stuck in local minima of multimodal functions. Thus, an approach that combines the strengths of stochastic and deterministic optimization schemes but avoids their weaknesses is of interest. This paper presents a new hybrid optimization algorithm that combines the PSO algorithm and gradient-based local search algorithms to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. In the new gradient-based PSO algorithm, referred to as the GPSO algorithm, the PSO algorithm is used for global exploration and a gradient based scheme is used for accurate local exploration. The global minimum is located by a process of finding progressively better local minima. The GPSO algorithm avoids the use of inertial weights and constriction coefficients which can cause the PSO algorithm to converge to a local minimum if improperly chosen. The De Jong test suite of benchmark optimization problems was used to test the new algorithm and facilitate comparison with the classical PSO algorithm. The GPSO algorithm is compared to four different refinements of the PSO algorithm from the literature and shown to converge faster to a significantly more accurate final solution for a variety of benchmark test functions.     

解决作业车间调度的微粒群退火算法*

针对微粒群优化算法在求解作业车间调度问题时存在的易早熟、搜索准确度差等缺点,在微粒群优化算法的基础上引入了模拟退火算法,从而使得算法同时具有全局搜索和跳出局部最优的能力,并且增加了对不可行解的优化,从而提高了算法的搜索效率;同时,在模拟退火算法中引入自适应温度衰变系数,使得SA算法能根据当前环境自动调整搜索条件,从而避免了微粒群优化算法易早熟的缺点。对经典JSP问题的仿真实验表明,与其他算法相比,该算法是一种切实可行、有效的方法。     

基于线性规划和遗传–粒子群算法的烧结配料多目标综合优化方法

针对钢铁企业二次配料工艺,本文采用将硫含量折算为可比成本,兼顾节能减排目标和配料成本,建立了二次配料多目标优化模型;提出了一种基于线性规划和遗传–粒子群算法（GA–PSO）的钢铁烧结配料优化方法.首先采用线性规划算法进行求解,若线性规划方法无法求得最优解,则采用GA–PSO算法进行搜索.该方法应用于某钢铁企业360m2生产线的＂配料优化与决策支持系统＂中,实际运行结果表明,该算法在保证烧结矿质量的前提下,能够有效地减少二氧化硫排放,降低配料成本.     

Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances over a wide range of loading conditions and parameter variations and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.     

结合MRF模型与粒子群优化算法的遥感影像纹理分割

影像纹理的马尔可夫随机场(MRF)模型是一种分析纹理较为经典的方法,已被广泛用于影像纹理的模拟和分割。由于传统的模拟退火算法在计算全局最优解时,处理效率较低,无法满足纹理分析与处理的性能要求。设计了一种判定纹理类别的适应度函数,提出了利用粒子群优化算法计算适应度函数的最优解,应用该算法对遥感影像数据进行了纹理分割实验。实验结果表明,该算法与模拟退火算法比较,具有寻优速度快的优点,是一种有效的图像分割优化方法。     

基于粒子群算法的传感器网络室内定位系统优化

通过接收信号强度指示(RSSI)和欧氏距离的非线性函数映射关系来进行弱移动目标的距离估计.以最大似然估计(MLE)法为基础,结合电磁波在自由空间中的传播特性,重新构建基于粒子群优化(PSO)定位算法的适应度函数来提高无线定位精度.并提出了一种惯性权重优化的自适应学习机制来优化全局搜索能力和局部搜索精度,提升定位算法的容错能力.测试结果表明:本室内定位算法具有抗干扰强、鲁棒性好和无线定位精度高等优点.     

基于遗传算法和粒子群优化的列车自动驾驶速度曲线优化方法

针对列车自动驾驶(ATO)过程中的精准停车、准时性、舒适性以及能耗问题,提出一种基于遗传算法与粒子群优化(GAPSO)算法结合的ATO速度曲线优化方法.首先,建立列车ATO运行多目标优化模型,将列车过分相区断电惰行纳入控制策略,并对运行控制策略进行分析;其次,对粒子群优化(PSO)算法进行改进,采用非线性动态惯性权重和...     

Reliability enhancement of a radial distribution system using coordinated aggregation based particle swarm optimization considering customer and energy based indices

This paper describes an algorithm for optimum modifications for failure rate and repair time for a radial electrical distribution system. The modifications are with respect to a penalty cost function minimization. The cost function has been minimized subject to the energy based and customer oriented indices, i.e. AENS, SAIFI, SAIDI and CAIDI. Coordinated aggregation based particle swarm optimization (CAPSO) has been used for optimization. The algorithm has been implemented on a sample radial distribution system. The results obtained have been compared with those obtained using PSO.     

Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers

This paper presents a new algorithm designed to find the optimal parameters of PID controller. The proposed algorithm is based on hybridizing between differential evolution (DE) and Particle Swarm Optimization with an aging leader and challengers (ALC-PSO) algorithms. The proposed algorithm (ALC-PSODE) is tested on twelve benchmark functions to confirm its performance. It is found that it can get better solution quality, higher success rate in finding the solution and yields in avoiding unstable convergence. Also, ALC-PSODE is used to tune PID controller in three tanks liquid level system which is a typical nonlinear control system. Compared to different PSO variants, genetic algorithm (GA), differential evolution (DE) and Ziegler–Nichols method; the proposed algorithm achieve the best results with least standard deviation for different swarm size. These results show that ALC-PSODE is more robust and efficient while keeping fast convergence.     

A new platform for the prediction of field-dependent yield stress and plastic viscosity of magnetorheological fluids using particle swarm optimization

The yield stress and plastic viscosity of magnetorheological (MR) fluids are identified by fitting rheological models based on a selected dataset on a certain range of shear rates. However, the datasets are often arbitrarily determined as there is no standardized procedure available. To overcome this problem, a platform that capable to minimize the fitting error while considering the classification of the shear rate regions is needed. Therefore, this work proposed a new platform for the systematic prediction of field-dependent rheological characteristics using particle swarm optimization (PSO). PSO is a meta-heuristic algorithm for solving optimization problems based on a guided search of the defined problem space, which is governed by the objective function. An intersection point of low and high shear rate regions critical shear rate is formulated as part of the objective function to standardize the characterization within the defined regions. The objective function is inspired by the modified Bingham biplastic and Papanastasiou models to predict five magnetic field dependent-rheological parameters. In the development stage, the shear stress model was first established using a previously developed extreme learning machine method. Then, the codes of the PSO, objective functions and search space identification were developed and implemented. To validate the effectiveness of the proposed procedure, the platform performance was analysed at different algorithmic parameters and compared with the existing optimization methods. The simulation results indicated that the proposed platform performed better than the existing ones with R² of 0.943 and was able to systematically and accurately predict the rheological parameters.