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Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China's shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard's block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise's block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers. [ABSTRACT FROM AUTHOR]

With the continuous expansion of urban scale in China, the increase of passenger flow has brought great pressure to the urban public transport system. An accurate and timely prediction of the short-term passenger flow at each metro station is extremely important for the metro intelligent control system to make a timely decision. In this paper, based on the measured passenger flow data of Zijingshan station of Zhengzhou Metro Line 1, an improved whale optimization algorithm is proposed to predict the passenger flow on different time scales. The results show that the method has higher accuracy than the traditional least squares support vector machines algorithm. The paper opens a new window for nowcasting warning in the rush hour and long-period optimization of the public transport. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned by gray wolf optimization, RVM tuned with a whale optimization algorithm, and RVM tuned with Manta Ray foraging optimization concerning root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R²) and Nash-Sutcliffe Efficiency (NSE) and new graphical inspection methods. The models were assessed using data acquired from two stations in China and data were divided into three equal parts. The models were tested using each data set. The application outcomes revealed that the proposed algorithm considerably improved the accuracy of a single RVM in monthly pan evaporation prediction by an average improvement in RMSE, MAE, R², and NSE as 27.65%, 27.53%, 8.40% and 8.63%, respectively. It is also found that the proposed algorithm showed significant dominance over others models with respect to improvement in overall mean values of RMSE, MAE, R², and NSE statistics from 34.7-38.2 to 18.2-19.5, 36.2-36.4 to 19.1-18.5, 12.5-13.8 to 3.6-3.7, and 12.4-14.6 to 3.6-3.9%, for both climatic stations, respectively. Importing extraterrestrial radiation and periodicity component (month number of the data) into the model inputs improved the prediction accuracy of the implemented models. The outcomes revealed that the RVM-IMRFO performed superior to the other methods in predicting monthly pan evaporation using only temperature data which is essential, especially in developing countries where other climatic data are missing or unavailable. The RVM model was also compared with standard multi-layer perceptron neural networks (MLPNN) and found that the first acts better than the latter in monthly pan evaporation prediction. [ABSTRACT FROM AUTHOR]

Group train operation (GTO) applications have reduced the tracking intervals for overloaded trains, and can affect the efficiency of rail transport. In this paper, we first analyze the differences between GTO and traditional operation (TO). A new mathematical model and simulated annealing algorithm are then used to study the problem of mixed group train operation. The optimization objective of this model is to maximize the transportation volume of special heavy-haul railway lines within the optimization period. The main constraint conditions are extracted from the maintenance time, the minimum ratio of freight volume, and the committed arrival time at each station. A simulated annealing algorithm is constructed to generate the mixed GTO plan. Through numerical experiments conducted on actual heavy-haul railway structures, we validate the effectiveness of the proposed model and meta-heuristic algorithm. The results of the first contrastive experiment show that the freight volume for group trains is 37.5% higher than that of traditional trains, and the second experiment shows a 30.6% reduction in the time during which the line is occupied by trains in GTO. These findings provide compelling evidence that GTO can effectively enhance the capacity and reduce the transportation time cost of special heavy-haul railway lines. [ABSTRACT FROM AUTHOR]

TBM method construction is an important accompanying construction form vigorously developed in China, and its anomaly detection is an important link to provide a basis for system operation and maintenance decision-making. The abnormal operation state of TBM caused by different surrounding rock geology, jamming or rock burst will directly affect the tunneling speed and ability, and then affect the system safety and construction progress. In this paper, a TBM construction anomaly detection method based on historical tunneling speed and other construction monitoring data is proposed. Firstly, the preprocessing steps based on outlier removal and correlation analysis are used to remove the noise in the original data and select the best features. Secondly, the variational mode decomposition is used to decompose the data into multiple modal components to extract the periodic and aperiodic features of TBM tunneling. Furthermore, an improved VMD-XGBoost-BILSTM combination model is constructed, and the characteristics of combination weighting, attention mechanism and improved whale optimization algorithm are used to realize the normal prediction of accurate and stable tunneling speed. Finally, by comparing with the actual measured values, the set rules are used to judge the anomalies. The experiments are carried out on the actual mining data of YE long-distance water conveyance tunnel in Xinjiang. The results show that the method proposed in this paper improves the RMSE by more than 20% compared with the single BILSTM or XGBoost model. The attention mechanism and IWOA algorithm bring 5.53% and 10.73% results improvement respectively, which can achieve the effect of early warning of different geological information changes. [ABSTRACT FROM AUTHOR]

One of the biggest difficulties, which China's power infrastructure is now experiencing, is the capacity to estimate demand of short-term electricity. It can accurately estimate changes in the total power load in particular locations. Accurate forecasting results, which also serve as a dependable guide for power system operation, may increase the flexibility and resource usage of the contemporary power market. Power load characteristics are impacted by several things. This study presents an estimate method for short-term demand of electricity based on WOA-BILSTM in order to comprehensively evaluate the time series features presented in the power load data and improve the accuracy of power load forecasting. After extracting features for the variables that influence power load, this method uses the bidirectional long-term and short-term memory (BILSTM) neural network layer for bidirectional time series feature learning. Using the local electricity load data from Quanzhou from 2018 as the data set, the prediction model is constructed by screening the multi-dimensional input parameters and selecting the feature vectors with good association as the input carefully. By comparing the outcomes of three popular load forecasting models, including LSTM network, BILSTM network, and WOA-BILSTM, it is demonstrated that the WOA-BILSTM neural network method is more accurate and effective than others two. By building an optimal combination model, this technique could increase the accuracy of power load data's short-term predictions. At the same time, it also reduces the time of personnel debugging. [ABSTRACT FROM AUTHOR]

Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R2 values of 0.96–0.97. In addition, the improvement in MAE (34.71–49.65%) and RMSE (32.82–48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction. [Display omitted] [ABSTRACT FROM AUTHOR]

With the wide use of smartphone travel apps, a new hybrid transit system (HTS) that integrates fixed-route transit (FRT) and appbased demand-responsive feeder transit (DRFT) services have recently emerged in many cities over the world. App-based DRFT services are coordinated with FRT to offer fast, comfortable, and well-connected mobility services to travelers. The study investigates a new variant of HTS, termed flexible HTS (FHTS), allowing FRT to have flexible vehicle departure times and capacitated vehicle loads and DRFT to have flexible feeder stations, so as to further enhance the coordination between FRT and DRFT. The coordinated operation optimization problem of such an FHTS in a travel corridor is formulated as a mixed-integer linear programming model. A novel hybrid metaheuristic algorithm is developed to solve it. In the hybrid algorithm, a constrained compass search method is used to optimize FRT vehicle departure times, and an adaptive large neighborhood search algorithm, coupled with a tailored forward time slack algorithm, is applied to solve the DRFT routing and scheduling problems. A case study in Shucheng County, China is conducted to examine the performance of the model and solution algorithms. Sensitivity analyses on feeder station layout and vehicle size are also conducted. The results show that flexible FRT vehicle departure times and flexible DRFT feeder stations have a great potential of enhancing the coordination of FRT and DRFT services by reducing operators' costs and passengers' total travel time. Managerial implications are to set feeder transit stations downstream of an FRT line and to keep a reasonable station spacing. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly being utilized as a global optimal method in the inversion and modeling of magnetic data. We proposed the Bat Algorithm Optimization (BAO) technique that is based on bat echolocation performance to find the global optimum solution. The best-estimated source parameters that correspond to the objective function minimum value are obtained after achieving the global optimum (best) solution. The suggested BAO technique does not require any prior knowledge; rather, it is a global search method that provides an effective tool for scanning the space of data to appraise sources parameters. The BAO technique is applied to magnetic data in the class of dipping and vertical dikes along 2D profiles to estimate the dimensional source parameters that include the depth to top, origin location, amplitude coefficient, index angle of magnetization, and width of the dipping dikes. The BAO technique has been used for single and multiple dikes structures. The accuracy and stability of the BAO technique are achieved on different synthetic examples of free and noisy data for single and multiple cases. Furthermore, the presented BAO technique was effectively utilized in three field examples from China and Egypt for iron ore deposits and metavolcanics basalt rock investigations. Overall, the BAO technique recovered inversion outcomes are in good agreement with borehole, geology, and published literature results. [ABSTRACT FROM AUTHOR]

Based on the basic principles of system dynamics, the mathematic description for the stock market evolution which is a typical complicated dynamic system is provided. And according to this mathematic description, a new hybrid intelligent method based on multi-layer feed-forward neural network (MFNN) which is the most widely used neural network and new improved black hole algorithm (IBHA) which is a new nature-inspired metaheuristic algorithm is proposed to model the stock market. In this new hybrid intelligent method, the new IBHA whose structure is simple and which can be implemented easily, and the modified back propagation algorithm have been used to optimize the architectural and algorithmic parameters of MFNN simultaneously. This new method is applied to model two data groups of Shanghai stock market in 2015.9–2018.8 of China to show that the computing results of the new method coincide well with the real data and that the forecasting precision is highly accurate in these cases. At last, the new method has been applied to model seven other stock market data sets and compared with five state-of-the-art hybrid algorithms proposed in previous studies. The results show that the performance of the new method (including modeling effect and efficiency) has been demonstrated to be satisfactory by using for different problem settings and market situations and it is also easy to be implemented. [ABSTRACT FROM AUTHOR]

Wind power generation has strong volatility. Accurate wind speed forecasting can not only avoid the waste of power resources, but also facilitate the development of clean energy and promote the energy transition worldwide. However, previous research has predominantly focused on the accuracy of wind power prediction, while ignoring the reliability of wind speed prediction system. In this research, a hybrid forecasting system with both accuracy and reliability of wind power forecasting is proposed. Firstly, a hybrid adaptive decomposition denoising algorithm is proposed to solve the unreasonable decomposition and residual noise. To improve the search performance, the seagull algorithm is optimized by chaotic system and Cauchy operator, and then the parameters of long short-term memory model are adjusted. Finally, based on data enhancement theory, an interval prediction model combined with kernel density estimation is proposed. The model is verified by the historical data of Sotavento wind farm in Spain and Eman wind farm in China. The average absolute percentage error values of wind speed point prediction are 2.87% and 8.01%, respectively. At the same confidence level, the interval prediction model proposed has narrower widths compared to the comparative model, with higher average interval scores. The results indicate that the point prediction model proposed in this research exhibits higher accuracy, while the interval prediction model demonstrates greater stability and reliability. These findings provide technical support for wind power forecasting. [Display omitted] • The HADD method is proposed to perform data decomposition and noise removal adaptively. • The SOA is improved using methods such as chaos system and Cauchy distribution. • Improved seagull search algorithm is applied to optimize parameters of LSTM model. • Interval prediction model based on data enhancement technology and KDE method. • A novel hybrid forecasting system is proposed considering the accuracy and reliability. [ABSTRACT FROM AUTHOR]

China's leisure diving tourism has made gratifying achievements in the past years. It is necessary to evaluate the leisure diving tourism's development level, and then the disadvantages of the leisure diving tourism can be found and the effective measures can be taken to develop the leisure diving tourism in China. A fuzzy comprehensive evaluation model based on the bat algorithm is proposed to solve the shortcomings of the fuzzy comprehensive evaluation method, which lacks a theoretical basis and completely depends on experience. Firstly, the basic conception of leisure diving tourism is discussed. Also, the main affecting factors of leisure diving tourism are analyzed. The evaluation model of leisure diving tourism developing level is constructed based on the fuzzy comprehensive method. The weight of the fuzzy comprehensive evaluation method is optimized by the bat algorithm to realize the optimal selection of weight. The fuzzy evaluation matrix is used to construct the judgment matrix to determine the weight of each evaluation index, and the bat algorithm is used to adaptively select the weight of the judgment matrix. Finally, three diving clubs are used as research objects to carry out evaluation analysis The results show that the fuzzy comprehensive evaluation method can effectively obtain the developing level of leisure diving tourism. [ABSTRACT FROM AUTHOR]

• Microgrid optimal dispatch to reduce costs, carbon emissions and power fluctuation. • The multi-objective optimization algorithm based on war strategy. • Pareto frontier sort based on membership degree. • Reasonable allocation of new energy and energy storage. This article introduces a novel metaheuristic multi-objective optimization algorithm to address the complex multi-objective optimization scheduling challenges of the microgrids for the first time. Comparing with common multi-objective optimization algorithms, the proposed Multi-Objective War Strategy Optimization (MOWSO) has the following highlights: 1) MOWSO only needs to set three parameters. The few parameters make the algorithm simple to debug and real-world implement in practical applications. 2) MOWSO avoids the local centralized distribution of Pareto frontier based on a random selection strategy. The selection range of multiple objectives is expanded properly. In addition, a comprehensive multi-objective optimization dispatch model for microgrids is established, concurrently considering operating costs, carbon emissions, and power fluctuations. Furthermore, the impact of new energy proportion and Energy Storage (ES) on microgrid optimal dispatch is thoroughly investigated. Finally, a case study about a village microgrid in North China is presented to validate the effectiveness and the superiority of proposed multi-objective optimization dispatch model and novel MOWSO algorithm. [ABSTRACT FROM AUTHOR]

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications. • A novel hybrid wind power forecasting system was proposed. • Solve problems of noise interference and high computing burden by data refining. • A strategy to fully extract nonlinear and linear data features was designed. • An enhanced multi-objective golden jackal algorithm was raised for optimization. • Pareto optimal solutions were theoretically proven to be achieved. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is of great significance to water resources management, disaster monitoring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective. [ABSTRACT FROM AUTHOR]

In response to poor economic efficiency caused by the single service mode of energy storage stations, a double-level dynamic game optimization method for shared energy storage systems in multiple application scenarios considering economic efficiency is proposed in this paper. By analyzing the needs of multiple stakeholders involved in grid auxiliary services, fully tap into the profitability potential of energy storage stations. The capacity of the shared energy storage system is optimized by the non-dominant sorting beluga whale optimization algorithm (NSBWOA) in the upper level, and the operation strategy under multiple scenarios is optimized by the adaptive greedy search algorithm (AGSA) in the lower level. With the goal of maximizing the gross annual total income and high-value peak regulation ratio, and minimizing the cost- income ratio, the optimal capacity configuration and operation strategy of the shared energy storage system are obtained through collaborative optimization between upper and lower level models. The effectiveness of the proposed method is verified through the simulation testing of actual operating data of a certain power grid in China. Simulation results show that the gross annual income and high-value peak regulation ratio across multiple scenarios (Scenario III) are the highest, and the cost-income ratio is at an acceptable low level, which can provide a theoretical basis for the large-scale application of energy storage systems in new power systems. • The profit relationship between multiple stakeholders in auxiliary services and energy storage needs is explored. • Double-level optimization control model for shared energy storage system in multiple application scenarios is established. • The combinatorial optimal scheduling problem in decision sets is solved by adaptive greedy search algorithm. • The non dominated sorting beluga whale optimization algorithm effectively solves multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is of great significance to water resources management, disaster monitoring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective. [ABSTRACT FROM AUTHOR]

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results. [ABSTRACT FROM AUTHOR]

Bats play important roles in ecosystems, and are thus considered bioindicators. Libraries of echolocation calls provide huge potential resources for bat species identifications, ecological studies and conservation surveys. Here, the echolocation calls of two morphologically similar bat species (Miniopterus magnater and Miniopterus fuliginosus) were recorded and described in order to characterize vocal signatures for field identification in China. Both M. magnater and M. fuliginosus emitted short frequency modulated echolocation calls with narrow bandwidths. Each call of the former species included two harmonics, with the first harmonic being the strongest, whereas calls of the latter species normally contained one harmonic. Although call durations were similar between the two species, there were significant differences in start, end and peak frequencies between M. magnater and M. fuliginous. The results showed that 92.3% of all calls recorded in China were attributed to the correct species based on spectral features of echolocation calls. We concluded that echolocation calls are valuable characters for the identification of morphologically similar bat species. [ABSTRACT FROM AUTHOR]

An accurate prediction of lake water levels is of great significance to water resource regulation, flood prevention and mitigation. However, water level fluctuations have been increasingly serious due to abnormal climate and extreme events. In view of this, a VMD-EF-OBILSTM model was constructed for lake water levels based on multiple sources of hydrological and meteorological variables. In this model, water level data are transformed into low-frequency internal and high-frequency external terms by variable modal decomposition (VMD), and they are combined with external factors (EF) for multivariate prediction. The optimized bi-directional long short-term memory (OBILSTM) invokes the attention mechanism and optimizes the model's hyperparameters by whale optimization algorithm (WOA). Ultimately, the predictions of each component are linearly combined to obtain the forecast values. The empirical results with water level data from Poyang Lake in China show that the multi-source deep learning model can achieve higher prediction accuracy and lower prediction uncertainty. • A deep learning model combined VMD-EF-OBILSTM for lake levels prediction is proposed. • Key parameters of the VMD can be adjusted automatically and optimally using the IWOA. • The adaptive function of IWOA based on E-EK can evaluate IMF features in several ways. • IMF is divided into internal and external terms for self and multivariate prediction. • The VMD-EF-OBILSTM includes external factors and performs better than standard models. [ABSTRACT FROM AUTHOR]

• Varying input patterns of reservoir operation are split by fuzzy clustering iteration. • Twin support vector regression (TSVR) models input–output relationship for reservoir operation policy. • Equilibrium optimizer finds suitable parameters for TSVR model within each pattern. • Proposed method provides satisfying operation results in two real-world reservoirs. Robust reservoir operation policies are crucial in ensuring the effective utilization of water resources. However, owing to multiple complicated and changeable factors in practice, it is difficult for standalone approaches to derive reasonable reservoir operation policy. To address the practical requirement, this research proposes a novel artificial intelligence method for deriving reservoir operation policy. The proposed method uses the fuzzy clustering iteration method to identify multiple typical operation patterns from the influencing factors; secondary, for all the samples within each pattern, the novel twin support vector regression (TSVR) is utilized to model the nonlinear mapping relationship between the influence inputs and the target outputs, while the emerging equilibrium optimizer is chosen to determine suitable computation parameters for the TSVR model. The feasibility of the proposed method is fully evaluated on two real-world huge hydropower reservoirs in China. The simulations demonstrate that the developed method can yield better comprehensive benefits than several control methods in deriving reservoir operation policy under uncertain environments. Hence, the experiments confirm that metaheuristic algorithms and pattern recognition techniques can enhance the performance of a standalone artificial intelligence methods in deriving reservoir operation policy. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three hybrid RF models were developed to predict blast-induced overbreak. • The RF-TSA model outperformed other predictive RF models in estimating overbreak. • The blast design parameters showed a deep impact on the overbreak generation. • The PF was selected as the most influential factor in the overbreak generation. Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects. [ABSTRACT FROM AUTHOR]

Accurate prediction of global solar radiation (R s) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting R s in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in R s estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R2) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R2) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R2) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting R s in various regions of China. •For standalone models, the SVM had higher prediction accuracy than DT and BP. •Three hybrid models (ACO-SVM, CS-SVM and GWO-SVM) were proposed to predict daily R s in different climatic zones of China. •The accuracy of hybrid models was higher than the standalone SVM model. •The GWO-SVM model performed better than the CS-SVM model, and the accuracy of both were better than ACO-SVM model. [ABSTRACT FROM AUTHOR]

Most extant studies on aviation's hub-and-spoke network focus on the optimization problems from the perspective of economies of scale thanks to the inherent inter-hub connections, whereas the economies of scale on each route is contingent on the associated allocation of different types of aircraft. In this paper, we devise an integrated model of hub-and-spoke network design and fleet planning with the constraints of the passenger flow demand as well as the adaptation of different types of aircraft for each route, with the objective to minimize the total system cost (i.e., the sum of the hub setting cost and aircraft's related cost). To tackle the complexity of the integrated model, we develop a heuristic solution algorithm based on a Genetic Algorithm framework and an improved Floyd–Warshall algorithm to solve the value of the fitness function. The proposed model and the developed algorithms are tested with the US flight network (i.e., the CAB dataset) and China's aviation network. The sensitivity analysis reveals that the demand window is one of the critical factors that affect hub-and-spoke network design and fleet planning, while the utilization rate of specified aircraft fleet determines the fleet configuration, which poses relatively less impact on hub-and-spoke network design. • Develop a heuristic solution algorithm based on a Genetic Algorithm framework and an improved Floyd–Warshall algorithm to solve the value of the fitness function. • The proposed model and the developed algorithms are tested with the US and China's aviation network. • Through our extensive computational experiments, it is shown that the integrated model can judiciously decimate the total cost by 5.24% on average compared with the traditional approach. [ABSTRACT FROM AUTHOR]

• Machine learning can help predict regional crop yield potential with fewer data. • Random forests-based meta-model had superior performance. • Geolocation and temperature are the essential environmental variables in the model. • Machine-learning-based meta-models cannot adapt to predict climate change impacts out of the range of test data. The crop growth model (CGM) widely contributes to studying the impacts of regional climate change on crop growth status and yield. However, it requires high-quality daily weather data for the model establishment and verification, and the low data availability limits the application of CGM at the regional scale. With the rapid development of machine learning techniques, adapting data-driven machine learning algorithms to build a meta-model with environmental feature variables at the desired spatio-temporal scale provides a new method for regional yield simulation. In this study, we developed four machine-learning-based meta-models to simulate regional yield potential (RYP) of wheat in China with the selected environmental feature variables and assessed four different machine learning algorithms, including multiple linear regression (MLR), artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The research aimed to verify whether these meta-models can outperform CGM in simulating RYP. The results showed that the meta-models could reduce the requirements of the number of input variables and the amount of data for RYP simulation and maintain the simulation accuracy because monthly weather variables could replace daily weather variables in the meta-models. Although all four meta-models can well reveal the mean RYP, the meta-model based on RF has the best performance. In the RF-based meta-model, longitude, latitude, altitude, and the averaged maximum temperature in March are the top four ranked essential variables. However, the generalizability of meta-models is affected by the training dataset, and the meta-models cannot adapt appropriately to new unseen data. Moreover, there is no unique optimal machine learning algorithm used for building meta-models, and this will increase the workload of similar research in the future. [ABSTRACT FROM AUTHOR]

• A carbon emission-compliance green location-inventory problem is studied • Uncertain demand and volatile carbon prices under a multi-year emission regulation inspired from the carbon-trading scheme in China. • A novel two-stage stochastic model for the ILIP with demand and carbon price uncertainties is developed. • The model is solved using an efficient three-phase hierarchical metaheuristic algorithm. • Managerial insights for both carbon emitters and the regulator are provided. In emerging markets, the growth of supply chain networks and logistics industry leads to a significant increase in energy consumption and induces high carbon emissions. To design a sustainable low-carbon supply chain network, we consider a carbon emission-compliance green location-inventory problem. It is characterized by uncertain demand and volatile carbon prices under a multi-year emission regulation, inspired from the carbon-trading scheme in China. A two-stage stochastic mathematical model is built and is solved with a three-phase hierarchical metaheuristic on extensive numerical experiments, which mimic the business context of a supply chain network operated in China. The results show to which extent the carbon-trading emission-compliance scheme, with uncertainties in demand and carbon price, impacts the strategic decisions. Besides, carbon emissions and supply chain profits of the design solutions produced under alternative emission regulations are evaluated and discussed. We also underline the sensitivity of the amount of carbon emissions to the demand uncertainty, and to the level and volatility of the carbon price in the carbon-trading system. These results provide managerial insights for supply chain emitters, and indicate that reasonable and stable carbon prices should be maintained by governments in emerging markets. [ABSTRACT FROM AUTHOR]

• Importance of metaheusristic approaches in spatial modelling and also accuracy. • Flood spatial modeling and its mapping using metaheuristic models. • Ensemble of ANFIS by GA, SA, ICA, and DE approaches. • Selection of the best combined ANFIS for flood susceptibility and hazard mapping. This study is performed to determine the importance of metaheuristic approaches and their accuracy with respect to the spatial modeling process emphasising on flood phenomena. In this study, four ensemble metaheuristic approaches, including the combinations of ANFIS (Adaptive Neuro-Fuzzy Inference System) with the genetic algorithm (GA), simulated annealing (SA), imperialist competitive algorithm (ICA), and differential evolution (DE), are used for flood zoning in Dingnan County, China. Based on historical records, aerial photo interpretation, field surveys, and Google Earth, the location of 115 floods are recorded in the study area. Different categorical and continuous factors affecting floods, including the plan curvature, altitude, distance to rivers, slope degree, rainfall, land use, lithology, stream power index, topographic wetness index, soil type, aspect, normalized difference vegetation index, and profile curvature, are identified in the study area and entered in GIS software. Flood susceptibility maps (FSMs) are validated using the ROC curve. The results confirm that the AUCs of the four combined metaheuristic models are larger than 79%. The highest AUC value is obtained for the ANFIS–GA ensemble (0.903), followed by the ANFIS–SA (0.843), ANFIS–DE (0.812), and ANFIS–ICA (0.798). The RMSEs obtained for the training data of the different models are 0.2562 (ANFIS-DE), 0.3121 (ANFIS-GA), 0.3345 (ANFIS-ICA), and 0.3398 (ANFIS-SA). The results of this study show that the proposed ensemble approaches are useful for flood hazard management and land use planning in Dingnan County, China, and other places. [ABSTRACT FROM AUTHOR]

One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the "optimal" parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping. [ABSTRACT FROM AUTHOR]