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.

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.

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.

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 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.

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.

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.

Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

Considering the increasing integration of renewable energies into the power grid,batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources.Besides,the deployment of batteries can increase the benefits of a renewable power plant.One way to increase the profits with batteries studied in this paper is performing energy arbitrage.This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high.In this paper,a hybrid renewable energy system consisting of wind and solar power with batteries is studied,and an optimization process is conducted in order to maximize the benefits regarding the dayahead production scheduling of the plant.A multi-objective cost function is proposed,which,on the one hand,maximizes the obtained profit,and,on the other hand,reduces the loss of value of the battery.A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective function.With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value,two more simplified cost functions are proposed.Results show the importance of including the energy efficiency in the cost function to optimize.Besides,it is proven that the battery lifetime increases substantially by using the multi-objective cost function,whereas the profitability is similar to the one obtained in case the loss of value is not considered.Finally,due to the small difference in price among hours in the analyzed Iberian electricity market,it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

Analytical Hybrid Particle Swarm Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Smart Grid

In this paper,the hybridization of standard particle swarm optimisation(PSO)with the analytical method(2/3 rd rule)is proposed,which is called as analytical hybrid PSO(AHPSO)algorithm used for the optimal siting and sizing of distribution generation.The proposed AHPSO algorithm is implemented to cater for uniformly distributed,increasingly distributed,centrally distributed,and randomly distributed loads in conventional power systems.To demonstrate the effectiveness of the proposed algorithm,the convergence speed and optimization performances of standard PSO and the proposed AHPSO algorithms are compared for two cases.In the first case,the performances of both the algorithms are compared for four different load distributions via an IEEE 10-bus system.In the second case,the performances of both the algorithms are compared for IEEE 10-bus,IEEE 33-bus,IEEE 69-bus systems,and a real distribution system of Korea.Simulation results show that the proposed AHPSO algorithm converges significantly faster than the standard PSO.The results of the proposed algorithm are compared with those of an analytical algorithm,and the results of them are similar.

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.

Delay-area trade-off for MPRM circuits based on hybrid discrete particle swarm optimization

Polarity optimization for mixed polarity Reed-Muller（MPRM） circuits is a combinatorial issue.Based on the study on discrete particle swarm optimization（DPSO） and mixed polarity,the corresponding relation between particle and mixed polarity is established,and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly,mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO（HDPSO）.Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model.Finally,the proposed algorithm is testified by MCNC Benchmarks.Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.

A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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。

Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the structure.Therefore,the accurate determination of crack characteristics,such as location and depth,is one of the key engineering issues for assessment of the reliability of structures.This paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle Swarm Optimization(PSO);these hybrids are Particle Swarm Optimization-Genetic Algorithm-Firefly Algorithm(PSO-GA-FA),Particle Swarm Optimization-Grey Wolf Optimization-Firefly Algorithm(PSO-GWO-FA),and Particle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization(PSO-GA-GWO).A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack parameters.Thus,the location and depth of a crack in a beam can be predicted by measuring its natural frequency.Hence,the measured natural frequency can be used as the input parameter of the algorithm.In this paper,this is applied to identify crack location and depth in a cantilever beam using the new hybrid algorithms.The results show that among the proposed triple hybrid algorithms,the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.

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.