Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

A Hybrid Differential Evolution Algorithm Integrated with Particle Swarm Optimization

To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.

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.

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.

Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

The design of an antenna requires a careful selection of its parameters to retain the desired performance.However,this task is time-consuming when the traditional approaches are employed,which represents a significant challenge.On the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance.In this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna.The proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep network.This optimized network is used to retrieve the metamaterial bandwidth given a set of features.In addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

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.

Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques

This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter Inertia Weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.

混合策略改进的粒子群算法

针对粒子群算法易陷入局部最优、收敛精度低、收敛速度慢等缺陷,提出了基于混合策略的改进粒子群算法。使用融合Circle映射与精英反向学习的策略初始化种群,提升初始种群的质量,同时加快收敛速度;在粒子速度更新方式中引入蜘蛛移动策略平衡算法的全局搜索与局部搜索;提出了基于自适应t分布的变异策略,增强算法全局搜索和跳出局部最优能力;对15个单峰和多峰函数进行仿真实验,与其他3种算法进行了对比分析,结果表明:所提出的改进算法具有很强的寻优能力与稳定性。

复杂多方向威胁下的导弹预警雷达优化部署方法

针对现有导弹预警雷达部署相对独立、协同困难,难以满足大规模对抗场景的现状,从远程预警雷达、跟踪识别雷达、机动式预警雷达不同的任务特点出发,建立应对复杂多方向威胁的多型导弹预警雷达优化部署模型,在满足最优覆盖、协同交接、目标识别等任务约束下,解决雷达协同部署问题。针对所提模型设计了一种基于云自适应的分区优化离散粒子群(CPBPSO)算法,通过设计分区编码策略缩减算法求解空间、加入云自适应变异算子提高算法全局寻优和局部跳出能力,使算法更适用于导弹预警雷达部署问题的处理。实例验证了所提模型在求解单方向、多方向威胁场景部署问题的可行性,对比分析了CPBPSO算法的有效性,基本满足导弹预警雷达最优化协同部署的需求。

基于改进T分布烟花-粒子群算法的AUV全局路径规划

针对传统粒子群算法在处理自主水下机器人(Autonomous Underwater Vehicle,AUV)全局路径规划时面临的寻优时间长、能耗高的问题,本文提出一种改进的T分布烟花-粒子群算法(T-distribution Fireworks-Particle Swarm Optimization Algorithm,TFWA-PSO),该算法融合了烟花算法的高效全局搜索能力和粒子群算法的快速局部寻优特性.在变异阶段,提出自适应T分布变异来扩大搜索范围,并在理论上证明了该变异方式能够使个体在局部最优解附近增强搜索能力.在选择阶段提出了适应度选择策略,淘汰适应度差的个体,解决了传统烟花算法易丢失优秀个体的问题,并对改进的T分布烟花算法与传统烟花算法的收敛速度进行对比.将改进算法的爆炸操作、变异操作和选择策略融合到粒子群算法中,对粒子群算法的速度更新公式进行了改进,同时从理论上对所改进的算法进行了收敛性证明.仿真实验结果表明,TFWA-PSO能够有效规划出一条最短路径,同时与给定的智能优化算法相比,TFWA-PSO在寻找最优路径的时间上平均降低了24.72%,能耗平均降低了17.33%,路径长度平均降低了16.96%.

公路隧道风光水储互补发电系统容量配置研究

为降低公路隧道的电力运营成本,探究可再生能源互补发电系统在公路隧道的应用前景,研究合适的容量配置求解方法。建立利用风、光、水和储能设备的互补发电系统为公路隧道提供电力资源。以特长公路隧道(总长7.1 km)为估算模型,采用改进后的粒子群优化算法,即离散型自适应粒子群优化算法,以全生命周期的建设成本和设备维护成本最小为目标函数,以缺电负荷率(LPSP)和储能电池的状态为约束,对风力发电设备、光伏发电设备、水力发电设备和储能设备的最优容量配置进行求解。结果表明:1)对比标准粒子群算法,离散型自适应粒子群优化算法的总投入成本更少,寻优能力更强;2)对比该隧道1年的用电成本,前期投入将在5年内回本;3)在风光水储互补发电系统的设备全生命使用周期的20年内,该隧道可节省1 920.39万元电费。

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

基于特征判定系数的电力变压器振动信号故障诊断

变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义。针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法。通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断。对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率。

基于改进粒子群算法优化的染色木材颜色检测算法研究

为提高染色木材颜色的检测精度和速度,对樟子松木材单板进行染色,选取染色单板的光谱反射率作为输入,以极限学习机模型为基础构建预测模型,对染色单板的色度参数L^(*)、a^(*)、b^(*)进行预测,运用粒子群算法对ELM权值和阈值进行寻优,并引入非线性惯性权重和新的位置与速度更新策略改进粒子群算法,以消除其易陷入局部最优的缺点。此外,以L^(*)、a^(*)、b^(*)平均绝对误差为评价指标,与基础ELM模型及其他模型作对比,发现优化后的模型平均绝对误差为0.16,测色效果相较于基础ELM的0.68、麻雀算法优化的ELM的0.37等具有明显优势,这对于提高木材染色生产效率具有重要意义。

疯狂自适应的正余弦乌燕鸥算法及应用

桥式起重机主梁优化是一个非线性的复杂约束优化问题,现有方法求解此问题时存在收敛缓慢、稳定性差、收敛精度低等问题。为克服此问题,提出一种疯狂自适应的正余弦乌燕鸥混合优化算法(CCASSTOA)。在乌燕鸥算法(STOA)中,引入Logistics混沌映射对STOA算法种群初始化,增加种群个体的多样性和迭代初期收敛速度;将惯性自适应权重和正余弦算法混合搜索策略引入到乌燕鸥位置更新公式中,增强了算法的全局搜索与局部搜索之间的平衡能力。将疯狂算子引入到乌燕鸥最优位置进行扰动,增强迭代后期种群的多样性,避免算法陷入局部最优。采用6个测试函数对CCASSTOA算法性能进行验证,结果表明:CCASSTOA算法优于其他五种元启发式优化算法,收敛精度高,稳定性好和鲁棒性强。将CCASSTOA算法应用在32t/22.5m的桥式起重机主梁轻量化设计中,可实现主梁截面面积减小约为31.45%。因此,CCASSTOA算法可有效地处理此类非线性的约束优化问题。