Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively,a new hybrid algorithm named CDEPSO is proposed in this work,which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search.In the CDEPSO algorithm,DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO,while the best solution in the PSO is performed by chaotic local search.To investigate the performance of CDEPSO,four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search).And,compared with the other six improved meta-heuristics,CDEPSO also exhibits more robust performance.In addition,a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics,and CDEPSO algorithm is the best in solving these problems.

Particle Swarm Optimization for Solving Sine-Gordan Equation

The term‘optimization’refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones.The majority of real-world situations can be modelled as an optimization problem.The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods.Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields.The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation,whose numerical solutions show the soliton form and has diverse applications.The implemented optimization technique is employed to determine the involved parameter in the basis functions,which was previously approximated as a random number in the work reported till now in the literature.The obtained results are comparable with the results obtained in the literature.The work is presented in the form of figures and tables and is found encouraging.

Optimal Static State Estimation Using hybrid Particle Swarm-Differential Evolution Based Optimization

In this paper, swarm optimization hybridized with differential evolution (PSO-DE) technique is proposed to solve static state estimation (SE) problem as a minimization problem. The proposed hybrid method is tested on IEEE 5-bus, 14-bus, 30-bus, 57-bus and 118-bus standard test systems along with 11-bus and 13-bus ill-conditioned test systems under different simulated conditions and the results are compared with the same, obtained using standard weighted least square state estimation (WLS-SE) technique and general particle swarm optimization (GPSO) based technique. The performance of the proposed optimization technique for SE, in terms of minimum value of the objective function and standard deviations of minimum values obtained in 100 runs, is found better as compared to the GPSO based technique. The statistical error analysis also shows the superiority of the proposed PSO-DE based technique over the other two techniques.

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 functions. The results show that the average performance of PSODE is the best.

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.

Hybrid Particle Swarm Optimization with Differential Evolution for Numerical and Engineering Optimization

In this paper, a hybrid particle swarm optimization （PSO） algorithm with differential evolution （DE） is proposed for numerical benchmark problems and optimization of active disturbance rejection controller （ADRC） parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world opti- mization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.

PID Neural Net work Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution

For complex systems with high nonlinearity and strong coupling,the decoupling control technology based on proportion integration differentiation(PID)neural network(PIDNN)is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem,a hybrid particle swarm optimization(PSO)and differential evolution(DE)algorithm(PSO-DE)is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show t hat the proposed met hod has better decoupling capabilities and control quality than the previous approaches.

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient-A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics differential evolution （DE） and particle swarm optimization （PSO）. In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

Most supply chain programming problems are restricted to the deterministic situations or stochastic environments.Considering twofold uncertainty combining grey and fuzzy factors,this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost.The programming parameters of the material suppliers,manufacturer,distribution centers,and the customers are integrated into the presented model.On the basis of the chance measure and the credibility of grey fuzzy variable,the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions.The designed neural network can expedite the simulation process after trained from the generated input-output data.The improved Particle Swarm Optimization(PSO) algorithm based on the Differential Evolution(DE) algorithm can optimize the uncertain programming problems.A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

Performance Evaluation and Comparison of Multi - Objective Optimization Algorithms for the Analytical Design of Switched Reluctance Machines

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.

电推进GEO卫星的改进粒子群轨道保持优化设计

针对地球同步轨道(GEO)卫星轨道保持问题,提出了一种基于改进粒子群算法(PSO)的序列电推力轨道保持方法。首先,建立了GEO卫星高精度非线性轨道动力学模型和序列电推力模型。然后,设计了GEO卫星相对轨道保持策略,建立了以燃料消耗为性能指标的序列电推力轨道保持问题优化模型并进行了离散化。接着,通过引入差分进化算法和维度学习策略对粒子群优化算法进行了适应性改进,同时对推力大小和作用时间进行寻优计算。最后,通过数值仿真对所提出的改进粒子群优化算法进行了对比校验。结果表明,该方法在完成GEO卫星轨道保持任务的同时具备燃料消耗低和收敛速度快等优点。

基于差分进化粒子群混合算法的多无人机协同区域搜索策略

为提高无人机群在未知环境中的区域搜索效率,提出一种多无人机协同区域搜索策略。首先,根据区域搜索任务需求,建立包含区域覆盖率、区域不确定度、目标存在概率三种属性的区域信息地图;其次,以最大化搜索效率、同时最小化无人机搜索过程中的能耗为目标,建立无人机区域搜索滚动时域优化目标函数,指导无人机在线决策搜索路线;然后针对传统群智能优化算法易陷入局部最优的缺陷,设计差分进化粒子群混合算法在线求解该多目标优化问题,提高算法的寻优性能,从而提高无人机的搜索效率。最后,通过数值仿真实验,对所提算法进行验证,仿真结果表明,文中设计的基于差分进化粒子群混合算法的多无人机协同区域搜索策略与传统的群智能优化算法相比具有更高的区域搜索效率。

多阶段多属性配电网规划项目优选模型及求解

目前配电网规划项目优选方法很少考虑负荷增长速度快慢对项目供电效益和项目入选年的影响,也很少同时考虑项目的紧急程度、技术效益和经济效益,为此建立以技术效益和经济效益最大为目标的中低压配电网规划项目优选模型。根据项目的紧急程度对项目技术效益进行奖优罚劣,为衡量项目供电效益的逐年变化速度,采用差异化权重法得到项目投产后5a的供电效益综合水平。提出改进的多目标粒子群算法求解模型,得到一系列目标互有优势的项目组合方案,根据供电企业的目标偏好对项目组合方案集进行多级筛选,得到最优项目组合方案。以某地区配电网规划项目库为例,优选结果表明所提模型能更好地考虑项目的紧急程度和供电效益变化速度,并实现多区域多目标综合提升。

改进教与学算法的静压推力滑动轴承优化

为了使静压推力滑动轴承在运行过程中功率损失最小,提出了改进的教与学算法(DWTLBO),对静压推力滑动轴承进行优化设计。与其它经典的智能优化算法如粒子群算法(PSO)、差分进化算法(DE)和教与学算法(TLBO)相比,该算法在学习阶段引入差分进化算子增加了各组之间的交叉率,进一步提高算法的多样性和局部搜索能力,避免早熟收敛。通过建立推力轴承模型,设计了轴承阶梯半径,油槽凹口半径,润滑油粘度,润滑油流量四个设计变量,采用改进的教与学算法对模型的相关参数进行优化。优化结果表明,提出的改进算法与传统的教与学算法相比,获得模型的最优解更佳,有利于在以后的工程优化中提高模型的设计精度。

基于混沌粒子群优化算法的反应釜温度预测控制研究

反应釜作为化工行业核心的生产容器,其温度控制优化在化工生产领域中具有重要作用。针对反应釜温度控制难的问题,提出了一种基于Tent映射的混沌粒子群优化(CPSO)算法优化动态矩阵控制(DMC)-比例积分微分(PID)的反应釜温度预测控制策略。由于DMC很难选取较优的参数,利用Tent映射的CPSO算法提高动态矩阵参数寻优的速度。通过试验,以及与常规PID、DMC-PID控制对比分析,基于Tent映射的CPSO-DMC-PID串级控制对温度控制系统有较好的控制精度和响应速度,可大幅缩小超调量。该控制策略对反应釜温度预测控制研究具有一定的参考意义。

基于微分平坦的分层轨迹规划算法

为充分考虑横纵向耦合和汽车运动学特性对轨迹规划的影响,提出一种分层优化的轨迹规划算法框架。利用贝塞尔曲线的凸包性设计安全走廊约束,以轨迹平滑性为目标函数得到一个基于贝塞尔曲线节点的下层规划器。在上层规划器中,基于下层规划器求解得到的横纵向贝塞尔曲线和车辆运动学模型的微分平坦输出进行三维耦合,构建满足车辆乘坐舒适性、高效性和安全性的目标函数,利用粒子群优化算法对贝塞尔轨迹初始参数进行二次优化得到综合性能最优的行驶轨迹。仿真结果表明:新算法在保证安全性的同时,具有良好的乘坐舒适性和可跟踪性;由于二次规划与粒子群优化算法的求解效率高,此框架实时性强,具有概率完备性。

基于图卷积网络和风速差分拟合的中长期风功率预测

为充分利用数据特征间的先验关系,提高风电场中长期发电功率预测精度,提出一种基于图卷积神经网络(GCN)、风速差分拟合(DF)、粒子群优化算法(PSO)的中长期风功率预测模型。通过分析风力发电全过程,挖掘风功率影响因素及因素间的相互关联性,搭建GCN模型,分别拟合风速和功率利用效率,进一步结合基于DF的风速-功率计算模型计算风功率,模型的损失包含功率损失、风速损失和功率利用效率损失3个部分,采用粒子群优化算法为这3部分损失确定合适的权重。2个风电场的实际算例表明,该模型未来10天风功率预测的相对均方根误差分别为11.44%和13.09%,具有较高的预测精度。

台风灾害下电网韧性评估及差异化规划

台风灾害易使电网产生群发性地断线从而演化为大型停电事故,造成巨大经济损失。文中针对极端台风灾害下电网韧性评估及差异化规划方法展开研究。首先,基于Batts风场模拟,构建电网线路故障恢复模型,提出一种考虑重要负荷供电韧性的评估指标。然后,构建差异化规划两阶段优化模型:第1阶段基于差异化规划方案优化线路加固策略;第2阶段通过优化储能供电支撑负荷恢复策略,进一步校验规划方案的韧性提升效果。最后,通过椭圆函数修正惯性权重的粒子群优化算法进行求解。通过仿真结果验证了所提方法的有效性。

基于VMD-ARIMA-DBN的短期电力负荷预测

针对短期电力负荷预测精度不足的问题,提出一种基于变分模态分解、深度信念网络、差分自回归移动平均模型的组合预测模型。首先选取电力负荷影响较大的相关参数,采用变分模态分解将负荷数据分解为低频和高频两种分量;然后利用差分自回归移动平均模型和深度信念网络分别对低频和高频两种分量进行预测,为克服深度信念网络参数随机化的缺陷,采用粒子群优化算法优化模型以进一步提高精度;最后组合各模型结果得到最终预测值。实验结果表明,该组合模型较其他模型具有更好的预测性能。