Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

Optimal Scheduling of Cascaded Hydrothermal Systems Using a New Improved Particle Swarm Optimization Technique

Optimum scheduling of hydrothermal plants generation is of great importance to electric utilities. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for solving short-term economic generation scheduling of hydrothermal systems to avoid premature convergence. A multi-reservoir cascaded hydrothermal system with nonlinear relationship between water discharge rate, power generation and net head is considered here. The performance of the proposed method is demonstrated on two test systems comprising of hydro and thermal units. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing better results.

Acceleration Factor Harmonious Particle Swarm Optimizer

A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to （ 1, 1）. Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO （AFHPSO） is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.

Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model

A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.

Fracture property identification method based on shrinkage factor particle swarm optimization

In the multi-wave and multi-component seismic exploration,shear-wave will be split into fast wave and slow wave,when it propagates in anisotropic media. Then the authors can predict polarization direction and density of crack and detect the development status of cracks underground according to shear-wave splitting phenomenon. The technology plays an important role and shows great potential in crack reservoir detection. In this study,the improved particle swarm optimization algorithm based on shrinkage factor is combined with the Pearson correlation coefficient method to obtain the fracture azimuth angle and density. The experimental results show that the modified method can improve the convergence rate,accuracy,anti-noise performance and computational efficiency.

Optimal Power Flow Solution Using Particle Swarm Optimization Technique with Global-Local Best Parameters

This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.

Integrating Tabu Search in Particle Swarm Optimization for the Frequency Assignment Problem

In this paper, we address one of the issues in the frequency assignment problem for cellular mobile networks in which we intend to minimize the interference levels when assigning frequencies from a limited frequency spectrum. In order to satisfy the increasing demand in such cellular mobile networks, we use a hybrid approach consisting of a Particle Swarm Optimization(PSO) combined with a Tabu Search(TS) algorithm. This approach takes both advantages of PSO efficiency in global optimization and TS in avoiding the premature convergence that would lead PSO to stagnate in a local minimum. Moreover, we propose a new efficient, simple, and inexpensive model for storing and evaluating solution's assignment. The purpose of this model reduces the solution's storage volume as well as the computations required to evaluate thesesolutions in comparison with the classical model. Our simulation results on the most known benchmarking instances prove the effectiveness of our proposed algorithm in comparison with previous related works in terms of convergence rate, the number of iterations, the solution storage volume and the running time required to converge to the optimal solution.

Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence optimization.However,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems.Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO.Subsequently,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution stages.To be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage.Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity.In addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method.Finally,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

Structural optimization strategy of pipe isolation tool by dynamic plugging process analysis

During the pipeline plugging process,both the pipeline and the pipe isolation tool(PIT)will be greatly damaged,due to the violent vibration of the flow field.In this study,it was proposed for the first time to reduce the vibration of the flow field during the plugging process by optimizing the surface structure of the PIT.Firstly,the central composite design(CCD)was used to obtain the optimization schemes,and the drag coefficient and pressure coefficient were proposed to evaluate the degree of flow field changes.Secondly,a series of computational fluid dynamics(CFD)simulations were performed to obtain the drag coefficient and pressure coefficient during dynamic plugging.And the mathematical model of drag coefficient and pressure coefficient with the surface structure of the PIT were established respectively.Then,a modified particle swarm optimization(PSO)was applied to predict the optimal value of the surface structure of the PIT.Finally,an experimental rig was built to verify the effectiveness of the optimization.The results showed that the improved method could reduce the flow field vibration by 49.56%.This study provides a reference for the design of the PIT surface structure for flow field vibration technology.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

一种基于PSO-ELM的低渗透砂岩水淹层测井识别方法

水淹层测井识别对油田开发方案部署及提高采收率有着重要意义。新疆陆梁油田作业区某区块油层水淹类型主要为污水水淹,测井响应特征复杂多变,传统识别图版方法难以对水淹层有效识别。文中基于测井、地质、试油等资料,在水淹层测井响应特征分析基础上,提出了一种利用改进粒子群优化算法(Particle Swarm Optimization,PSO)及极限学习机(Extreme Learning Machine,ELM)的水淹层识别方法。首先,利用相关系数优选6个主控因素:RD,RS,GR,SP,DEN,AC。其次,采用改进粒子群算法对极限学习机模型进行参数寻优;最后,利用优化后的模型对研究区水淹层进行预测。结果表明,利用PSO-ELM模型识别水淹层,识别符合率达到91.7%,应用效果优于ELM模型及传统识别图版,为水淹层测井识别提供了新思路。

基于Armstrong能量模型的非线性动态维拉里磁滞行为建模与验证

磁致伸缩材料工作在应力激励条件下的输出特性在很大程度上取决于偏置条件(预应力、偏置磁场)和激励频率。为指导磁致伸缩材料在动态应力驱动下的应用,需要建立一个能够适应各种操作条件的动态磁滞模型。该文结合Armstrong能量模型和J-A磁滞模型,建立了磁致伸缩材料的静态维拉里磁滞模型,通过引入频率相关时间常数的一阶微分方程将静态模型拓展为考虑动态损耗的动态非线性维拉里磁滞模型。利用粒子群遗传优化算法通过三个递进步骤提取模型参数。实验数据与模型计算数据的对比结果表明,该模型不仅能够充分描述预应力和偏置磁场对准静态维拉里效应的影响,而且能够反映在不同频率动态应力下磁通密度-应力(B-σ)动态小环和主环的变化趋势,该模型可为磁致伸缩材料器件在应力条件下的应用提供理论指导。

引信步进应力加速试验贮存寿命预测研究

目的 针对某机电引信加速寿命试验数据,采用传统统计分析方法存在计算量大、寿命预测精度难以保证的问题,开展与智能算法相结合的引信贮存寿命预测研究。方法 针对步进应力加速寿命试验数据,采用贝叶斯理论的环境因子法,对各级应力下的贮存时间进行折合计算。利用进化策略对粒子群算法进行改进,进而对所建立的BP神经网络预测模型的全局参数进行调整和优化,突破传统方法的局限。将折合后的试验时间、样本量、应力水平作为网络输入,失效数作为输出,来预测引信贮存寿命。结果 利用训练好的BP神经网络预测引信在正常应力水平下的失效数,计算其贮存可靠度。在迭代402次后,模型找到最优解,且预测误差在1%以内。结论 步进应力加速寿命试验与智能算法相结合的方法计算过程简单,预测精度较高,可有效提高引信贮存寿命的预测精度。

基于改进粒子群算法的阻尼惯量自适应控制策略

针对传统虚拟同步发电机控制策略存在暂态调节时间长及稳定性差等问题,提出一种基于改进粒子群算法的阻尼惯量自适应控制策略。首先,通过分析系统受扰动后功角特性,提出阻尼惯量自适应控制策略;然后,利用改进粒子群算法选择控制策略初始值,给出关键参数的选取原则及具体范围;最后,通过与现有控制策略进行对比,分析不同惯量及阻尼下对系统影响并验证控制策略的优越性。结果表明,该策略可有效提高系统稳定性及动态响应性能。

融合寻优算法的双馈风力机控制参数分步辨识方法

为获得准确的双馈风力机(DFIG)控制参数以提高电力系统机电/电磁仿真分析和计算的准确性,将长短期记忆(LSTM)神经网络与改进粒子群(IPSO)算法相结合对DFIG的控制参数进行辨识。首先,利用RT-LAB平台通过硬件在环(HIL)实验获得真实DFIG控制器的响应数据集;其次,为避免无关特征干扰LSTM模型的预测结果,利用最大信息系数提取出DFIG中高相关性的观测量特征;在此基础上,为提高算法的寻优速度,利用LSTM初步寻优到DFIG控制参数的初始值与搜索范围;最后,通过IPSO算法精确辨识出DFIG的控制参数,提高了辨识算法的寻优效率和精度。HIL测试结果证实了LSTM-IPSO辨识方法在20%~80%低电压穿越工况下具有良好的适应性,并能有效提高DFIG控制参数的辨识精度。

基于MFO-BPNN的螺旋钻机钻速预测研究

针对利用现有经验公式所建立的螺旋钻机钻速预测模型存在准确度不足的问题,提出了一种基于飞蛾扑火算法(MFO)的反向传播神经网络(BPNN)钻速预测模型。首先,对MFO算法的基本原理进行了研究,构建了MFO算法优化BPNN的具体流程;接着,采集了江苏无锡某施工现场钻探数据,并分析了钻速影响因素,运用小波阈值降噪、归一化和灰色关联度分析等系列方法对采集数据进行了预处理,得到了训练和测试集;然后,将MFO算法运用于神经网络的权值和阈值训练,以代替原有梯度下降法,建立了MFO-BPNN钻速预测模型;最后,对上述预测模型与BPNN模型、遗传算法优化反向传播神经网络(GA-BPNN)模型以及粒子群优化算法优化反向传播神经网络(PSO-BPNN)模型的预测结果和评价指标进行了详细的对比分析。研究结果表明:运用MFO-BPNN建立的钻速预测模型,其可靠性达到了91.65%,其决定系数(R 2)优于其他3种预测模型,3项误差指标也是其中最低的,说明该模型的预测精度良好,适合于桩基础工程的实际应用,可为复杂因素影响下的钻速预测提供一种新思路。

极限学习决策网络指导的多目标粒子群算法

在求解多目标优化问题时,粒子群优化算法通常采用预设的榜样选择方法和搜索策略,无法根据具体的寻优状态进行调整。面对不同的优化问题,不合适的搜索策略难以有效指导种群的进化,导致种群的搜索性能降低。为了解决以上问题,提出一种极限学习决策网络指导的多目标粒子群优化算法(ELDN-PSO)。首先,将多目标优化问题分解成若干标量子问题,并构建一个极限学习决策网络。网络将粒子的位置作为输入,根据当前寻优状态为每个粒子选择合适的搜索动作。将粒子在子问题上的适应度值变化作为强化学习的样本用于训练网络,并通过极限学习机提升训练速度。在优化的过程中,网络会根据寻优状态自动调整,在不同的搜索阶段为粒子选择合适的搜索策略。其次,多目标优化问题中存在一系列难以比较的非支配解,将每个解的领导能力量化成可进行比较的数值,从而更明确地为粒子选择合适的学习榜样。此外,使用一个外部档案储存较好的粒子,用于维护解集质量并指导种群的进化。在ZDT和DTLZ测试函数上进行对比实验,结果表明ELDN-PSO能够有效应对不同形状的Pareto前沿,提升种群的寻优速度以及解集的收敛性和多样性。