Strong Global Search Ability Research Articles

Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models

Given strong global search ability and less sensitive to initial solutions, many metaheuristic algorithms have been successful used to extract the unknown parameters of photovoltaic (PV) models. However, most applied metaheuristic algorithms need extra control parameters except the essential population size and terminal condition. For unknown optimization problems, how to set these control parameters to get the optimal solutions is a great challenge. To overcome this challenge, this paper presents a novel metaheuristic algorithm called comprehensive learning Jaya algorithm (CLJAYA) for parameter extraction of PV models. CLJAYA is a new variant of Jaya algorithm, which enhances global search ability of Jaya algorithm by the designed comprehensive learning mechanism. CLJAYA has a simple structure and only needs the essential population size and terminal condition for optimization. To verify the effectiveness of the improved strategies, CLJAYA is first employed to solve the well-known CEC 2015 test suite. Then the performance of CLJAYA is investigated by extracting the unknown parameters of three PV models including single diode model, double diode model and PV module model. Experimental results prove the superiority of CLJAYA on these test cases in terms of accuracy and efficiency by comparing with Jaya algorithm and other competitive algorithms.

Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area

In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

Short-term traffic flow prediction based on improved wavelet neural network

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%.

A Temperature Error Parallel Processing Model for MEMS Gyroscope based on a Novel Fusion Algorithm

To deal with the influence of temperature drift for a Micro-Electro-Mechanical System (MEMS) gyroscope, this paper proposes a new temperature error parallel processing method based on a novel fusion algorithm. Firstly, immune based particle swarm optimization (IPSO) is employed for optimal parameters search for Variational Modal Decomposition (VMD). Then, we can get the optimal decomposition parameters, wherein permutation entropy (PE) is employed as the fitness function of the particles. Then, the improved VMD is performed on the output signal of the gyro to obtain intrinsic mode functions (IMFs). After judging by sample entropy (SE), the IMFs are divided into three categories: noise term, mixed term and feature term, which are processed differently. Filter the mixed term and compensate the feature term at the same time. Finally, reconstruct them and get the result. Compared with other optimization algorithms, IPSO has a stronger global search ability and faster convergence speed. After Back propagation neural network (BP) is enhanced by Adaptive boosting (Adaboost), it becomes a strong learner and a better model, which can approach the real value with higher precision. The experimental result shows that the novel parallel method proposed in this paper can effectively solve the problem of temperature errors.

A Novel Clustering Algorithm Based on DPC and PSO

Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cut-off distance ${d_{c}}$ value. To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations. First, to solve the effect of the selection of the parameter ${d_ {c}} $ on the calculation density and the clustering results, this paper proposes a method to calculate that parameter. Second, a new fitness criterion function is proposed that iteratively searches ${K} $ global optimal solutions through the PSO algorithm, that is, the initial cluster centers. Third, each sample is assigned to ${K} $ initial center points according to the minimum distance principle. Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers. Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets. The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter ${d_{c}}$ . Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect.

Optimization of Transportation Routing Problem for Fresh Food by Improved Ant Colony Algorithm Based on Tabu Search

With the continuous improvement of people’s living standards and their increasing demand for fresh food, the cold chain logistics industry has developed rapidly. One of the biggest challenges to today’s cold chain logistics is to offer fresh food while minimizing CO2 emissions. The fresh degree and CO2 emissions are involved in the vehicle routing optimization problem in the cold chain logistics. In order to meet the quality requirement for fresh agricultural products and low carbon logistics, a novel routing optimization model considering the costs of quality deterioration and carbon emissions (Low Carbon and Freshness Degrees Vehicle Routing Problem (LCFD-VRP)) for cold chain distribution was established in this study. This model takes into account the fixed cost, fuel cost and time window penalty cost. An improved ant colony algorithm (IACA) is used to optimize the whole vehicle distribution routing with its strong global search ability. Tabu Search (TS) algorithm is used to search the single vehicle distribution routing with its good local search ability. An IACA combined with TS (IACATS) was proposed to solve the above LCFD-VRP model. The practicability of the model and the effectiveness of the above improved algorithm are verified using a real case study. The results of Zhoushan Dayang Refrigerated Logistics Co., Ltd. showed that, compared with the traditional algorithm, IACATS could reduce the dispatching of two refrigerated vehicles, thus lowering the total cost by 4.94%, shortening the actual transportation distance by 5.50% and cutting the total CO2 emissions by 8.9%. Therefore, the LCFD-VRP model can effectively help to achieve the low carbon emissions, multi-variety and low-cost distribution of fresh agricultural products. The proposed model and IACATS algorithm would be used to optimize VRP in cold chain enterprises. The results of this study also provide management suggestions for cold chain enterprises to effectively balance economic cost and environmental cost.

Optimum Design and Analysis of U-shape Thin-wall Aqueduct Based on Genetic Algorithm

In order to save project investment and reduce project quantity, the optimal design of aqueduct is the main problem in the project therefore, carrying out the optimal design and simulation analysis of U-shaped thin-walled aqueduct has important engineering application value. The paper takes Sha River aqueduct as the research object and adopts the direct search and point checking method of mixed discrete variable optimisation design. It also adopts the structural optimisation design method based on genetic algorithm and gives the optimal design scheme of U-shaped aqueduct. The optimisation design method has the advantages of fast convergence speed, high convergence efficiency and strong global search ability. The results show that, U-shape aqueduct structure is smooth, it has good mechanical properties, and the structural stress is small and can meet the strength requirement. The maximum vertical displacement of the aqueduct occurs in the middle of the span, the vertical displacement value is small, which can meet the requirement of stiffness.

Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA

With the development of computer science, many machine learning algorithms such as the back propagation (BP) neural network algorithm have been applied to the prediction of coal and gas outburst. However, the BP neural network algorithm can easily fall into the local minimum and has a low convergence rate. In this study, the genetic algorithm (GA) with strong global search ability and the simulated annealing algorithm (SA) with strong local search ability were integrated into a new GASA algorithm. This algorithm, which boasts strong spatial search ability and serves to optimize the initial weight and threshold of BP neural network, succeeds in solving the problem of easily falling into the local minimum. Furthermore, an improved GASA-BP prediction model was established by introducing an adaptive learning rate into the original BP neural network algorithm. The model is applied to coal and gas outburst prediction in Weicun Coal Mine in Jiaozuo City, Henan Province, China. Experimental results show that compared with the BP and GA-BP models, the GASA-BP model can predict coal and gas outburst accurately and quickly.

Location Problem Based on Immune Optimization

This paper presents the site selection of disaster response and rescue system, uses cloud logistics coverage model to construct multi-objective programming, uses immune optimization algorithm to simulate biological immune system to reach the optimal two-dimensional coordinates of address are (130.73, 80.43) and so on. It shortens a lot of rescue time and has a positive impact. cloud logistics coverage model reduces service radius and increases transportation flexibility. Immune optimization algorithm has stronger global search ability than genetic algorithm.

PSO/GA Combined with Charge Simulation Method for the Electric Field Under Transmission Lines in 3D Calculation Model

The accurate calculation of electric field intensity under transmission lines is more and more important with the expansion of high voltage engineering, which largely determines the site selection and design of transmission projects. In order to get more accurate results, a methodology of a charge simulation method (CSM) combined with an intelligent optimization algorithm for a 3D calculation model is proposed in this paper. Three key points are emphasized for special mention in this optimized charge simulation method (OCSM). First, the number of sub-segments on the finite length conductor, the position and number of the simulating charge set on a sub-segment are taken as the optimization parameters for unified calculation. Secondly, the fitness function of optimization algorithm is constructed by two values, voltage relative error and electric field intensity relative error. Thirdly, a finite element method (FEM) was used to obtain the electric field intensities, which are compared with the results of the proposed algorithm. A simulation case is carried out on a 3D calculation model of 220 kV transmission lines, which verify the effectiveness of the optimization algorithm. The proposed OCSM solves the parameter optimization problem of CSM in the 3D computational model, which considers physical shape of wire span, and has the advantages of strong global search ability and higher calculation accuracy.

A Particle Swarm Optimization Algorithm Based on Dynamic Adaptive and Chaotic Search

Although the particle swarm optimization algorithm has the advantages of fast convergence, easy to use and strong versatility, the algorithm also has the defects of low search precision, poor local search ability and easy to fall into local optimal solution. Therefore, this paper proposes a particle swarm optimization algorithm based on dynamic adaptive and chaotic search to ensure the global search ability of the particle swarm while avoiding falling into the local optimal solution. The experimental results show that compared with the comparison algorithm, the DACSPSO has stronger global search ability, higher convergence precision, and can effectively avoid premature convergence.

Study on short-term network forecasting based on SVM-MFA algorithm

Accurate prediction of power supply load is vital in power industry, which provides economic operation decision for the power operation department. For the unpredictability and periodicity of power load, nonlinear intelligent forecasting method is adopted. A modified firefly algorithm (MFA) combined with support vector machine (SVM) is proposed to predict the load of power supply data in this paper. The nonlinear mapping function is used to deal with the nonlinear regression problem in SVM, in which the parameters affect the accuracy of load prediction, so the MFA method is adopted to optimized the parameters of SVM. In order to verify the accuracy of SVM-MFA, mean absolute percentage error (eMAPE) was used as the fitness function for simulation and comparison experiment. The results show that the SVM-MFA proposed in this paper has stronger global search ability and faster convergence rate than the traditional artificial neural network, and it is verified that the method proposed in this paper has higher accuracy and higher stability of network load prediction.

A significance-based trust-aware recommendation approach

Trust-aware recommender systems have been widely used in recent years to improve the performance of traditional collaborative filtering systems. A common assumption of existing trust models is that all items have the same importance for all users. However, it is reasonable to expect that some items are more significant than others in making recommendations. Furthermore, the significance of an item is not the same for all users but varies depending on many factors such as the demographic characteristics of users. For example, an item that is important to women may not be important to men. Also, the significance of an item for an individual user is not static and can change throughout the life cycle (from childhood to old age). Thus, items that are currently important to a user may become less important in the future. In this paper, we propose a Significance-Based Trust-Aware Recommendation (SBTAR) approach, which uses a new trust measure based on the concept of item significance. The significance of an item for a user is measured with respect to the demographic context of the user. Thus, SBTAR can adapt to dynamic changes in user preferences. To model demographic context, SBTAR uses Shuffled Frog Leaping Algorithm (SFLA), which is a meta-heuristic optimization technique based on the social behavior of frogs. SFLA has the advantages of simplicity, fast convergence, strong global search ability and easy implementation. Experimental results show that the proposed approach is more effective and efficient than several state-of-the-art recommendation approaches.

Coking energy consumption radial basis function prediction model improved by differential evolution algorithm

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits.

A decomposition and statistical learning based many-objective artificial bee colony optimizer

Encouraged by the strong global search ability of the artificial bee colony (ABC) optimizer in single-objective problems (SOPs), we extend ABC to solve many-objective optimization problems (MaOPs) by exploiting the potential of direction vectors. Specifically, direction vectors are used not only to transform the original MaOP into a set of SOPs, but also to divide the bee colony into multiple subpopulations, while ABC serves the purpose of SOPs optimization. However, search equation of ABC is not efficient in convergence speed due to the overrated exploration property, to alleviate this issue, we suggest a search path statistical learning mechanism to predict the potential solutions, which is utilized to strengthen the exploitive search and accelerate the convergence rate. In addition, a new adaptive scalarization approach by exploiting population entropy and knee point information is developed to determine the elite in each subpopulation. For onlooker bee stage, a new fitness assignment scheme is proposed to achieve the computational effort allocation among food sources. The proposed algorithm is compared with several popular evolutionary optimizers on a wide range of test problems covering varying complexity, and empirical results show that it is very competitive and promising.

Study on Fault Diagnosis of Mining Tape Conveyor Based on MPGA-BP

In order to detect the malfunction of the mine tape transporter drive device imely, a fault diagnosis model of mine tape transporter based on multiple group genetic neural networks is established by combining multiple swarm genetic algorithms with BP neural network algorithm. The simulation results show that the algorithm can effectively solve the problems such as the easy fall of BP neural network into local minimum value and too many training times, which leads to too slow convergence. The fault diagnosis model has strong global search ability, high training precision and fast convergence speed, and can supplement and perfect the protection system of mine tape conveyor.

Anomaly Detection Algorithm Based on FCM with improved Krill Herd

The anomaly detection algorithm plays an important role in the field of network security. Among them, the most representative method is anomaly detection based on fuzzy C-means (FCM). FCM relies heavily on the initial clustering center and is prone to local extremum. Therefore, the detection effect of the FCM-based anomaly detection algorithm is not ideal in some cases. The herd intelligent optimization technology has a strong global search capability and is widely used in various fields. As a herd intelligence technology, the krill herd algorithm has a relatively simple optimization function structure, which has strong global search ability and is easy to integrate with other optimization strategies. Therefore, a KH algorithm with strong global search capability is introduced, and a hybrid KH-FCM algorithm is proposed. In the hybrid KH-FCM algorithm, the randomly generated initial population will be divided into two subpopulations containing the same number of individuals.

A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo’s nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

Implementation and simulation analysis of GMPPT algorithm under partial shadow condition

Abstract Due to partial occlusion, the output power and voltage curves of PV arrays appear multiple peak points, which increase the difficulty of designing the maximum power tracking system. The limitations of the conventional GMPPT method under the partial shadow are analyzed, and the failure of the traditional optimization algorithm is obtained. The optimization algorithm with strong global search ability can be used to solve the problem. A quantum-behaved particle swarm optimization (QPSO) algorithm is proposed and the implementation process of the algorithm is given. Finally, the standard particle swarm optimization (PSO) algorithm is compared with QPSO. It shows that QPSO is more suitable for solving the photovoltaic MPPT questions under partial shadow, effectively improving the photovoltaic utilization and generating electricity.

An adaptive artificial-fish-swarm-inspired fuzzy C-means algorithm

Fuzzy C-means (FCM) is a classical algorithm of cluster analysis which has been applied to many fields including artificial intelligence, pattern recognition, data aggregation and their applications in software engineering, image processing, IoT, etc. However, it is sensitive to the initial value selection and prone to get local extremum. The classification effect is also unsatisfactory which limits its applications severely. Therefore, this paper introduces the artificial-fish-swarm algorithm (AFSA) which has strong global search ability and adds an adaptive mechanism to make it adaptively adjust the scope of visual value, improves its local and global optimization ability, and reduces the number of algorithm iterations. Then it is applied to the improved FCM which is based on the Mahalanobis distance, named as adaptive AFSA-inspired FCM(AAFSA-FCM). The optimal solution obtained by adaptive AFSA (AAFSA) is used for FCM cluster analysis to solve the problems mentioned above and improve clustering performance. Experiments show that the proposed algorithm has better clustering effect and classification performance with lower computing cost which can be better to apply to every relevant area, such as IoT, network analysis, and abnormal detection.