Hybrid optimization algorithm based on chaos,cloud and particle swarm optimization algorithm

As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.

Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.

A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,respectively.Statistical performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

Traveling Salesman Problem Using an Enhanced Hybrid Swarm Optimization Algorithm

The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

基于HPSOGA的多目标电动汽车充电优化

随着电动汽车保有量的快速上升,电动汽车无序并网充电将会给配电网负荷平稳带来巨大的不确定性,因此对电动汽车的充电进行优化十分重要。为此,提出一种基于混合粒子群优化遗传算法(hybrid particle swarm optimization genetic algorithm,HPSOGA)的多目标电动汽车充电优化策略。使用Monte Carlo法基于用户出行规律建立电动汽车充电负荷曲线,在传统PSO算法的基础上引入GA算法的迭代机制,形成HPSOGA算法并用其对以用户充电费用最少和电网负荷波动率最小建立的多目标优化模型进行求解。结合具体算例进行仿真分析,结果显示基于HPSOGA算法的多目标电动汽车充电优化策略具有更快的优化速度以及更好的优化效果,进一步降低电网负荷峰值、提高电网负荷谷值,电网负荷波动率得到有效降低,同时用户充电成本得到有效减少。

Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization

The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

Convolutional neural networks(CNNs)have been developed quickly in many real-world fields.However,CNN’s performance depends heavily on its hyperparameters,while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons:(1)the problem of mixed-variable encoding for different types of hyperparameters in CNNs,(2)expensive computational costs in evaluating candidate hyperparameter configuration,and(3)the problem of ensuring convergence rates and model performance during hyperparameter search.To overcome these problems and challenges,a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the Gaussian process and particle swarm optimization(GPPSO)algorithm.First,a new encoding method is designed to efficiently deal with the CNN hyperparameter mixed-variable problem.Second,a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations.Third,a novel activation function is suggested to improve the model performance and ensure the convergence rate.Intensive experiments are performed on image-classification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods.Moreover,a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications.Experimental results demonstrate the effectiveness and efficiency of GPPSO,achieving accuracy of 95.26%and 76.36%only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets,respectively.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

基于HPSO的三相PWM整流器ADRC参数整定

三相PWM整流器采用电流内环、电压外环的控制方式,将电压外环的PI控制器替换为自抗扰控制器ADRC使得输出的电压稳态精度更高、动态性能更优异。然而在Simulink软件中进行控制参数整定时,ADRC控制参数过多导致PSO对于多极值问题容易收敛到一个局部最优解,需要多次仿真,从而降低了整定效率。混合粒子群算法HPSO虽然相对于PSO收敛速度慢,但是可以很好地解决PSO收敛到局部最优解的问题。仿真结果验证了HPSO具有较强的全局和局部寻优能力,且整定的参数相对于PSO能使系统获得更好的动态性能。

基于PSO与AFSA的GNSS整周模糊度种群融合优化算法

载波相位测量是实现全球导航卫星系统(Global navigation satellite system, GNSS)快速高精度定位的重要途径,而准确解算整周模糊度是其中的关键步骤之一.粒子群算法(Particle swarm optimization, PSO)收敛速度快但易陷入局部最优,人工鱼群算法(Artificial fish swarm algorithm, AFSA)全局优化性能好但收敛速度慢,因此融合两种算法的优点,提出一种GNSS整周模糊度种群融合优化算法(PSOAF).首先,通过载波相位双差方程求解整周模糊度的浮点解和对应的协方差矩阵.然后,采用反整数Cholesky算法对模糊度浮点解作降相关处理.其次,针对整数最小二乘估计的不足通过优化适应度函数来提高算法的收敛性和搜索性能.最后,通过PSOAF算法对整周模糊度进行解算.通过经典算例和试验研究表明:PSOAF算法可以更快地收敛于最优解,搜索效率也更为出色,解算的基线精度可以控制在10 mm以内,在短基线的实际情况下具有较高的应用价值.

具有紧时、高能耗特征的混合流水车间多目标调度优化问题

针对具有紧时、高能耗工序特征的混合流水车间调度问题,以优化产品暴露时间、最大完工时间和能源消耗为目标,建立混合流水车间调度模型,并提出一种改进的多目标粒子群算法进行有效求解。首先构建了基于ISDE指标的档案维护策略及局部邻域搜索策略,辅助算法跃出局部极值及减少生产阻塞。之后,提出一种基于模糊理论的决策分析方法选取最优调度方案。最后,通过仿真实验验证提出的多目标调度模型与算法的可行性和优越性。

基于分组学习粒子群算法的众包软件项目调度

为解决众包软件项目调度问题中的开发者选择、任务分配和投入度确定3个强耦合子问题,引入开发者信誉度,考虑技能、工作时长、开发团队规模等约束,以项目完成质量和工期为目标建立数学模型。提出一种采用三段式混合编码的分组学习粒子群算法求解所建模型。所提算法根据适应度排序将种群划分为3组,不同分组的粒子数量随进化代数自适应变化,且各组根据不同的适应度采用不同的更新策略。将所提算法与10种具有代表性的算法在12个不同规模的众包软件项目调度算例中进行对比,结果表明,所提算法能够获得精度更高的调度方案。

基于混合粒子群算法的波浪能发电集群优化方法

对波浪能发电集群的优化控制有助于波浪能的有效利用,为此文章提出了基于混合粒子群算法的波浪能发电集群优化方法。以直驱式发电装置为研究对象,探讨其构成发电集群短期尺度下稳定状态的数学模型,由简至繁依次考虑波浪动态压力、装置间辐射影响和遮挡效应,以便更准确地模拟一定密集度的波浪能发电装置部署下的实际效果。以集群功率最大化为优化目标,根据装置运动和海域能量约束,提出混合粒子群算法求解集群的最优参数,在传统算法基础上设定自适应惯性权重并加入交叉和变异操作,以应对复杂集群方程解空间的多峰性问题。算例结果验证了所述集群优化方法的有效性,求解质量良好;同时表明波浪能发电集群规模越大,装置之间的辐射影响越复杂,遮挡效应越明显。

运输能力受限下分段建造的时空调度问题

分段建造作为船舶整个建造流程的关键环节之一,其空间资源和时间资源的动态协调性严重影响着船舶建造周期和订单交付。针对分段建造船体车间空间有限、时间组织和空间组织不协调,以及以往分段建造时空调度相关研究忽略运输能力限制等问题,以最大完工时间最小、总提前与延期惩罚值最小为优化目标,以空间大小、加工小组的异质性、运输设备的差异性与运输限制为约束条件,构建多目标时空调度模型,并基于交付期优先规则、空间定位规则和关键阻碍规则提出混合粒子群算法进行求解,最终以某船厂分段建造为例进行实例验证,结果表明:所提出的模型与算法有效降低了分段完工时间,提高了分段建造的准时化水平。研究结可为船舶智能制造以及分段制造执行系统(MES)的实际应用提供一种方法基础。

无人机17kW电机振动噪声分析与巡航转速下尖端噪声优化

随着无人机的迅速发展,噪声问题影响消费者体验及AI交互、语音识别等技术,限制了无人机应用潜力。该文针对一台17 kW无人机用外转子永磁同步电机进行研究。为降低电机尖端振动噪声,且保留原电机电磁性能,重点提出优化磁极和定子开槽的方法。具体以平均转矩、转矩脉动等作为约束条件,构建多目标优化数学模型,并利用混合粒子群优化算法求解。该文深入探讨磁极参数、定子开槽对低阶次径向气隙磁通密度空间谐波特征的影响。并对电机转子模态仿真,以研究径向电磁力与空间模态的作用机理。在多转速情况下,以巡航转速为重点,分析整体电机电磁振动噪声特征。最后,仿真和实验结果表明,电机在巡航转速下的尖端噪声显著减小。验证了优化结构对无人机电机尖端振动噪声有明显抑制作用,对解决无人机噪声问题具有重要意义。

无人机遥感反演小麦地上生物量模型的特征选择

无人机多光谱技术能快速、无损地测定小麦地上生物量(AGB)。然而,多光谱方法在计算植被特征时会产生大量具有高度相关的重复特征。因此,建立结构简单、精度高的模型对特征进行筛选具有重要意义。本文提出了一种可以同时实现特征筛选与参数优化的混合编码灰狼粒子群优化算法(CGWOPSO)。同时,为评估基于该算法驱动的极限梯度提升模型(CGWOPSO-XGB)的性能,将其及基于两种流行特征筛选方法(Pearson和SHAP方法)的模型(P-XGB和S-XGB)的反演AGB表现进行了对比。结果表明,S-XGB模型优于P-XGB模型,前者均方根误差(RMSE)比后者低3.0%~16.3%;而CGWOPSO-XGB模型精度高于S-XGB模型,前者RMSE比后者低16.0%。