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•A novel grey forecasting method is proposed to predict the production and sales of China’s new energy vehicles.•Accumulation and translation transformations are introduced into the ...grey buffer operator.•A genetic algorithm is used to optimise the parameters of the new operator to ensure high-precision prediction.•The proposed method exhibits much higher prediction accuracy than those of the classical grey forecasting methods.
In recent years, under a series of policy shocks, the production and sales of new energy vehicles in China show the characteristics of trend mutation and non-smoothness. In order to forecast the production and sales of new energy vehicles in China, an optimised grey buffer operator is proposed by introducing accumulation and translation transformations. Meanwhile, a genetic algorithm is employed to ascertain its optimum parameters. Forecasting results indicate that the optimised buffer operator can significantly improve the adaptability of the grey model to the production and sales data of new energy vehicles in China, and exhibits much higher prediction accuracy than those of the classical buffer operator and grey model. Besides, the prediction results show that the production and sales of China’s new energy vehicles will continue to grow from 2018 to 2020, with an average annual growth rate of 27.53% and 30.49%, respectively.
•A Qutrit Genetic Algorithm is proposed for thresholding of hyperspectral images.•A new quantum mutation procedure is implemented to bring diversity in the offsprings.•Interactive Information method ...and BS-NET-Conv Network reduce the number of bands.•Modified Otsu Criterion and Masi entropy are used as fitness functions.•A quantum disaster operation prevents the population to get stuck into local optima.
Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and Sørensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency.
Recent decades have seen increased interest in using the controlled rocking concept in seismic resisting systems. Unlike conventional systems, where lateral deformation of a member is achieved ...through the formation of plastic hinges in critical regions, in the rocking systems this is achieved through a gap opening mechanism. Due to gravity load and/or post-tensioning forces, the rocking systems exhibit a self-centering behavior. Conducting a continuum finite element analysis to investigate the seismic response of such a system is quite expensive in terms of computational resources. On the other hand, a simplified macro model using two springs to simulate the gap opening/closing mechanism cannot accurately predict the dynamic response of the system. This study utilizes a multiple-spring model to simulate the nonlinear seismic response of circular tubular steel piers. An efficient optimization procedure based on a genetic algorithm is developed to calibrate the parameters of the springs. The results of continuum finite element analyses are compared with those obtained from the multi-spring model to verify the accuracy of the model. The proposed method is shown to be advantageous for accurately simulating the seismic response of a bridge model subjected to multi-directional ground motions, particularly the hysteretic force-displacement relationship, and dynamic response time history.
•Seismic response of posttensioned (PT) rocking steel bridge piers is investigated through continuum and macro finite element (FE) modeling approaches.•Computationally efficient macro modeling approaches, i.e., two and multi -spring macro models, are discussed.•A procedure for calibrating the parameters of the multi-spring model using genetic algorithm is presented.•The performance of two-spring and multi-spring macro models in predicting the seismic response is examined.•Multi-spring macro model is extended to simulated the response of double rocking configuration.
This paper presents application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques to estimate oil demand in Iran, based on socio-economic indicators. The models are developed ...in two forms (exponential and linear) and applied to forecast oil demand in Iran. PSO–DEM and GA–DEM (PSO and GA demand estimation models) are developed to estimate the future oil demand values based on population, GDP (gross domestic product), import and export data. Oil consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the average relative errors on testing data were 2.83% and 1.72% for GA–DEMexponential and GA–DEMlinear, respectively. The corresponding values for PSO were 1.40% and 1.36% for PSO–DEMexponential and PSO–DEMlinear, respectively. Oil demand in Iran is forecasted up to year 2030.
Wind power ramp events (WPREs) have received increasing attention in recent years as they have the potential to impact the reliability of power grid operations. In this paper, a novel WPRE ...forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual wind power data from a wind power plant in the Bonneville Power Administration (BPA) were selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.
Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on ...the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.
This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid ...genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.
In building design and renovation, achieving thermal comfort and enhancing energy saving are two crucial objectives. To obtain trade-off solutions between these two objectives, a multi-objective ...optimization approach was proposed to optimize the key parameters of exterior wall coatings through the integration of Non-dominated Sorting Genetic Algorithm II with EnergyPlus. Three optimization scenarios (i.e., prioritizing energy saving, prioritizing thermal comfort, and prioritizing energy saving and thermal comfort equally) were investigated to help decision-makers design coatings. The results indicated that under the first scenario, the energy savings account for 21 % of comparison building (cooling energy intensity: 2.83 kWh·m−2·a−1, thermal comfort time: 4195 h), while the thermal comfort time is 4061 h. Under the second scenario, the thermal comfort time is 7 % greater, while the cooling energy intensity is 3.07 kWh·m−2·a−1. Under the third scenario, the energy savings are 8 %, and the thermal comfort time is 4295 h. Moreover, the effects of thermal parameters of coatings on thermal comfort and energy savings were investigated. Low thermal conductivity and longwave emissivity are beneficial for these two objectives. Hence, thermal conductivity and longwave emissivity values of 0.05 and 0.85, respectively, are recommended. However, the effects of solar reflectance on these two objectives are opposite. A high solar reflectance is beneficial for energy savings and unbeneficial for thermal comfort, so its value should be determined according to the objective priority. The investigate results could provide significant guidance for parameter design of exterior wall coatings for residential buildings located in hot summer and warm winter regions.
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•A multi-objective optimization design method for exterior wall coatings is proposed.•Optimization is performed aiming at energy saving and thermal comfort objectives.•Guidance values for the key parameters of exterior wall coatings are provided.
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•An intelligent modelling framework is proposed for mechanical properties of CPB.•This framework combines machine learning (ML) algorithms and genetic algorithm.•1077 UCS tests and ...231 UTS tests were conducted to prepare dataset.•The performance of three advanced ML algorithms was verified and compared.•A software, the IMB, was developed for a wider application of this framework.
The mechanical properties of cemented paste backfill (CPB) are particularly important for its application in the minerals industry. In practice, a large number of cumbersome and time-consuming experiments are required to generate the design data. To facilitate the CPB design, this study proposes an intelligent modelling framework for the mechanical properties prediction using machine learning (ML) algorithms and genetic algorithm (GA). Three advanced ML algorithms, including decision tree (DT), gradient boosting machine (GBM), and random forest (RF), were used and compared for the mechanical properties modelling while GA was used for the hyper-parameters tuning. A total of 1077 uniaxial compressive strength (UCS) tests and 231 uniaxial tensile strength (UTS) tests were performed for the dataset preparation. Mechanical properties evaluated were the UCS, the yield strength (YS), the Young’s modulus (E) and the UTS. Influencing variables for these mechanical properties were chosen to be the physical and chemical characteristics of tailings, the cement-tailings ratio, the solids content, and the curing time. The results show that GA was efficient in the hyper-parameters tuning of the evaluated ML algorithms. The GBM was a good first ML algorithm for the mechanical properties modelling with high accuracy (correlation coefficients between predicted and experimental properties were 0.963, 0.887, 0.866 and 0.899 for UCS, YS, E and UTS respectively). Based on the results, a user-friendly software package, named the intelligent mining for backfill (IMB), was developed in python programming for a wider application in the minerals industry. The proposed modelling framework and the IMB will be useful for CPB design by saving time, reducing trial tests and cutting costs.
In this paper, two different architectures based on completely and sectionally clustered arrays are proposed to improve the array patterns. In the wholly clustered arrays, all elements of the ...ordinary array are divided into multiple unequal ascending clusters. In the sectionally clustered arrays, two types of architectures are proposed by dividing a part of the array into clusters based on the position of specific elements. In the first architecture of sectionally clustered arrays, only those elements that are located on the sides of the array are grouped into unequal ascending clusters, and other elements located in the center are left as individual and unoptimized items (i.e. uniform excitation). In the second architecture, only some of the elements close the center are grouped into unequal ascending clusters, and the side elements were left individually and without optimization. The research proves that the sectionally clustered architecture has many advantages compared to the completely clustered structure, in terms of the complexity of the solution. Simulation results show that PSLL in the side clustered array can be reduced to more than -28 dB for an array of 40 elements. The PSLL was -17 dB in the case of a centrally clustered array, whereas the complexity percentage in the wholly clustered array method was 12.5%, while the same parameter for the partially clustered array method equaled 10%.
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