Optimization Algorithm Research Articles

Crystal Symmetry-Inspired Algorithm for Optimal Design of Contemporary Mono Passivated Emitter and Rear Cell Solar Photovoltaic Modules

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels.

Crystal Symmetry-Inspired Algorithm for Optimal Design of Contemporary Mono Passivated Emitter and Rear Cell Solar Photovoltaic Modules

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels.

A hybrid quantum–classical algorithm for mixed-integer optimization in power systems

Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power system applications. The main issue of these formulations is the computational complexity of the solution algorithms, as they are considered NP-Hard problems. Quantum computing has been tested as a potential solution towards reducing the computational burden imposed by these problems, providing promising results, motivating the can be used to speedup the solution of MILPs. In this work, we present a general framework for solving power system optimization problems with a Quantum Computer (QC), which leverages mathematical tools and QCs’ sampling ability to provide accelerated solutions. Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow. Specifically, using an accelerated version of Benders Decomposition , we split a given MILP into an Integer Master Problem and a linear Subproblem and solve it through a hybrid “quantum–classical” approach, getting the best of both worlds. We provide 2 use cases, and benchmark the developed framework against other classical and hybrid methodologies, to demonstrate the opportunities and challenges of hybrid quantum–classical algorithms for power system mixed integer optimization problems.

Invasive weed optimization based compact hybridized fractal antenna design for RF energy harvesting and multifunctional wireless applications

This paper describes the optimal design and analysis of compact multiband Hybrid Fractal Antenna using Invasive Weed Optimization (HFAIWO). The configured structure involves the fusion of Minkowski, Sierpinski carpet and Giuseppe Peano in a coherent manner. The two-iterative conglomerated fractal antenna is realized physically by considering FR4 epoxy material (dielectric constant, εr = 4.4 and mass density = 1,900 kg/m3). The volumetric dimensions of the fabricated prototype are 36 x 36 x 1.6 mm3. In the designing process, the geometrical descriptors, namely, feedline width ‘WF’ and ground plane dimension ‘LG’ are optimized specifically using Invasive Weed Optimization (IWO) and Harmony Search Algorithm (HSA) strategies. The optimal solution is searched by changing the descriptor values under a set of practical constraints. The examined values of ‘WF’ and ‘LG’ using IWO and HSA are 3 and 31 mm, and 2.6 and 28.7 mm, respectively. Comparative results reveal that IWO offers superior solution quality and convergence than HSA. Afterwards, experimentation of the Projected HFAIWO is done to justify the recommended fractal hybridization approach. Measurements reveal that for VSWR ≤ 2, the fabricated structure produces nine resonance points (1.84, 3.99, 5.15, 5.55, 6.30, 6.58, 8.00, 9.29, and 12.01 GHz) with appreciable gain values. At the respective resonance points, the −10 dB impedance bandwidth is 3.9 %, 3.1 %, 2.1 %, 2.2 %, 1.4 %, 1.2 %, 6.9 %, 1.2 %, and 12.23 %. The provided radiation patterns are arbitrary bidirectional/omnidirectional. By applying the above-said approaches, the radiator size is reduced by 54 %. Importantly, the constructed structure covers two important bands i.e. 1.84 GHz (GSM 1800 band, downlink) and 5.55 GHz (WLAN (Lower) 5.150–5.725 GHz) associated with energy harvesting applications. The practical outcomes suggest that the introduced prototype is a proficient candidate for energy harvesting systems, Satellite communication for downlink, Long range tracking, Battlefield surveillance, Digital broadcast satellite service, Weather Radar, and Maritime navigation radar.

Deep reinforcement learning-based dynamic multi-beam power allocation for GEO-LEO co-existing satellites

This paper first formulates a novel long-term beam power allocation (BPA) problem to tackle the harmful co-linear interference issue in the geostationary earth orbit (GEO) and low earth orbit (LEO) co-existing satellite system. This BPA problem intends to optimize the long-term weighted sum rate of the LEO system while ensuring that GEO user’s received interference from the LEO satellite system is lower than a pre-fixed threshold. To solve it in a real-time manner, a deep reinforcement learning (DRL) framework based on the proximal policy optimization (PPO) algorithm is proposed, named as drlBPA. In addition, for the existing most relevant baseline, the fractional optimization (FO)-based BPA scheme, on the one hand, this paper improves it via a greedy strategy to fully exploit time resource. On the other hand, to further reduce the computational complexity stemming from its iterative solving procedure, a deep neural network approximation scheme is also developed. Simulation results demonstrate that (i) The trained DRL model of the proposed drlBPA scheme has good convergence and generality performance. (ii) Compared with the three FO-based benchmarks, the drlBPA scheme not only achieves the highest throughput of the LEO system within a significantly reduced computation time, but also yields the best system stability.

Research on the identification method based on energy distribution of adaptive WPD for railway tunnel lining void

ABSTRACT As the main disease of railway tunnels, void disease affects the safe operation of railways. The traditional detection technology recognition void relies on the intuitive characteristics of the signal, there are still problems such as poor void edge recognition capability and low intelligence level. To solve this problem, this study used Comsol software to establish a sound-solid coupling finite element model of the tunnel, extracted the acoustic signals of voids under various conditions, analysed the acoustic signal characteristics under different conditions and proposed the acoustic sensitive frequency band of tunnel lining void. The test model of Partial tunnel lining is established by the method of layering pouring. The wavelet packet algorithm is improved by the maximum cross correlation coefficient, the optimal wavelet basis decomposition algorithm is proposed. The layer number of the wavelet packet decomposition (WPD) algorithm was calculated based on the sensitive frequency band and the energy distribution of the sub-signal is obtained. Then, the energy distribution of the sensitive frequency band is used to estimate the void boundary. The experimental results show that the acoustic frequency band of the tunnel void mainly distributes in the range of 0–10,000 Hz and the sensitive frequency band of the void concentrated around 0–3000 Hz. According to the sampling frequency and the sensitive frequency band of the void, the sub-signal energy distribution of each layer is obtained. The energy distribution of sub-signal 1 has strong void boundary recognition ability. The research results will be beneficial to the damage detection and state assessment of tunnel lining.

Robust optimization of a novel ultraviolet (UV) photoreactor for water disinfection: A neural network approach

To optimize the ultraviolet (UV) water disinfection process, it is crucial to determine the ideal geometric dimensions of a corresponding model that enhance performance while minimizing the impact of uncertain photoreactor inputs. As water treatment directly affects people's lives, it is crucial to eliminate the risks associated with the non-ideal performance of disinfection photoreactors. Input uncertainties greatly affect photoreactor performance, making it essential to develop a robust optimization algorithm in advance to mitigate these effects and minimize the physical and financial resources required for constructing the photoreactors. In the suggested algorithm, a two-objective genetic algorithm is integrated with a non-intrusive polynomial chaos expansion (PCE) technique. Additionally, the Sobol sampling method is employed to select the necessary samples for understanding the system's behavior. An artificial neural network surrogate model is trained using sufficient data points derived from computational fluid dynamics (CFD) simulations. A novel type of UV photoreactors working based on exterior reflectors is chosen to optimize the process with three uncertain input parameters, including UV lamp power, UV transmittance of water, and diffusive fraction of the reflective surface. In addition, four geometrical design variables are considered to find the optimal configuration of the photoreactor. The standard deviation (SD) and the reciprocal of log reduction value (LRV) are set as the objective functions, calculated using PCE. The optimal design provides a LRV of 3.95 with SD of 0.2. The coefficient of variation (CoV) of the model significantly declines up to 7%, indicating the decreased sensitivity of the photoreactor to the input uncertainties. Additionally, it is discovered that the robust model exhibits minimal sensitivity to changes in reflectivity in various flow rates, and its output variability aligns with the SD obtained through robust optimization.

A Performance and Data-Driven Method for Optimization of Traditional Courtyards

As urbanization and rapid industrialization accelerate, rural areas face increasing pressure on resources and the environment, leading to challenges such as energy waste and reduced comfort. Traditional village planning and design methods are based on economic benefits and often lack consideration of climate adaptability. To address these issues, a comprehensive assessment of building and courtyard performance should be introduced early in the planning of traditional villages. This approach can better adapt the buildings to their climatic conditions. Introducing relevant performance indicators, such as outdoor comfort, indoor lighting, and building energy consumption, at the initial design stage is crucial. This article employs performance-based multi-objective optimization algorithms and machine learning techniques to investigate the design workflow of courtyards and their combinations. The goal is to enhance planners’ design efficiency in village planning by integrating data-driven and performance-driven methods. The research results show that during the performance-driven phase, by adjusting the spatial morphology and architectural parameters, the performance of the courtyard significantly improved compared to the baseline model. Energy efficiency increased by 32.3%, the physiological equivalent temperature (PET) comfort time ratio in winter was enhanced by 8.3%, and the ratio in summer increased by 3.8%. During the data-driven phase, the classification prediction accuracy of courtyard performance can reach 83%, and the F1 score is 0.81. In the project validation phase, it has also been proven that the performance of different plans can be quickly verified. Compared to the base’s original status, the design solutions’ performance score can be improved from 59.12 to 85.62. In summary, this workflow improves the efficiency of the interaction between design decisions and performance evaluation in the conceptual stage of village planning, providing a solid foundation for promoting subsequent solutions.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

A Performance and Data-Driven Method for Optimization of Traditional Courtyards

As urbanization and rapid industrialization accelerate, rural areas face increasing pressure on resources and the environment, leading to challenges such as energy waste and reduced comfort. Traditional village planning and design methods are based on economic benefits and often lack consideration of climate adaptability. To address these issues, a comprehensive assessment of building and courtyard performance should be introduced early in the planning of traditional villages. This approach can better adapt the buildings to their climatic conditions. Introducing relevant performance indicators, such as outdoor comfort, indoor lighting, and building energy consumption, at the initial design stage is crucial. This article employs performance-based multi-objective optimization algorithms and machine learning techniques to investigate the design workflow of courtyards and their combinations. The goal is to enhance planners’ design efficiency in village planning by integrating data-driven and performance-driven methods. The research results show that during the performance-driven phase, by adjusting the spatial morphology and architectural parameters, the performance of the courtyard significantly improved compared to the baseline model. Energy efficiency increased by 32.3%, the physiological equivalent temperature (PET) comfort time ratio in winter was enhanced by 8.3%, and the ratio in summer increased by 3.8%. During the data-driven phase, the classification prediction accuracy of courtyard performance can reach 83%, and the F1 score is 0.81. In the project validation phase, it has also been proven that the performance of different plans can be quickly verified. Compared to the base’s original status, the design solutions’ performance score can be improved from 59.12 to 85.62. In summary, this workflow improves the efficiency of the interaction between design decisions and performance evaluation in the conceptual stage of village planning, providing a solid foundation for promoting subsequent solutions.

A novel optimization rainfall coupling model based on stepwise decomposition technique

Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

Optimizing Storage Efficiency Through Pipelining and Parallelization for Enhanced Data Deduplication

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom’05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management.

Improved density prediction model based on global optimization algorithm for GPR system

Density prediction models are key to pavement compaction and density estimation using ground penetrating radar (GPR). However, current models do not account for the effects of dielectric constant errors and rely on limited data for parameter fitting. This article introduces an electromagnetic (EM) mixing theory and genetic algorithm (GA) based density prediction model named EM-GA. A comprehensive density database is established from literature, laboratory and field tests. After comparing Bayesian optimization, GA, simulated annealing (SA), and particle swarm optimization (PSO), the GA, which performs the best, is selected. On the testing set, the EM-GA model outperforms traditional models, with a notable reduction of 43.6 % in Mean Absolute Percentage Error (MAPE) and 39.8 % in Root Mean Square Error (RMSE). More importantly, the EM-GA predictions are accurate in the validation of four continuous survey lines. The proposed EM-GA model effectively enhances the accuracy of GPR-based density estimation for newly constructed asphalt pavement.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

An Obstacle Avoidance Trajectory Planning Methodology Based on Energy Minimization (OTPEM) for the Tilt-Wing eVTOL in the Takeoff Phase

Electric tilt-wing flying cars are an efficient, economical, and environmentally friendly solution to urban traffic congestion and travel efficiency issues. This article addresses the high energy consumption and obstacle interference during the takeoff phase of the tilt-wing eVTOL (electric Vertical Takeoff and Landing), proposing a trajectory planning method based on energy minimization and obstacle avoidance. Firstly, based on the dynamics analysis, the relationship between energy consumption, spatial trajectory, and obstacles is sorted out and the decision variables for the trajectory planning problem with obstacle avoidance are determined. Secondly, based on the power discretization during the takeoff phase, the energy minimization objective function is established and the constraints of performance limitations and spatial obstacles are derived. Thirdly, by integrating the optimization model with the SLSQP (Sequential Least Squares Quadratic Programming algorithm), the second-order sequential quadratic programming model and decision variable update equations are derived, establishing the solution process for the trajectory planning problem of the tilt-wing eVTOL takeoff with obstacle avoidance. Finally, the Airbus Vahana A3 is taken as an example to verify and validate the effectiveness, stability, and robustness of the model and optimization algorithm proposed. The validation results show that the OTPEM (obstacle avoidance trajectory planning methodology based on energy minimization) can effectively handle changes in the takeoff end state and exhibits good stability and robustness in different obstacle environments. It can provide a certain reference for the three-dimensional obstacle avoidance trajectory planning of Airbus Vahana A3 and other tilt-wing eVTOL trajectory planning problems.

An Obstacle Avoidance Trajectory Planning Methodology Based on Energy Minimization (OTPEM) for the Tilt-Wing eVTOL in the Takeoff Phase

Electric tilt-wing flying cars are an efficient, economical, and environmentally friendly solution to urban traffic congestion and travel efficiency issues. This article addresses the high energy consumption and obstacle interference during the takeoff phase of the tilt-wing eVTOL (electric Vertical Takeoff and Landing), proposing a trajectory planning method based on energy minimization and obstacle avoidance. Firstly, based on the dynamics analysis, the relationship between energy consumption, spatial trajectory, and obstacles is sorted out and the decision variables for the trajectory planning problem with obstacle avoidance are determined. Secondly, based on the power discretization during the takeoff phase, the energy minimization objective function is established and the constraints of performance limitations and spatial obstacles are derived. Thirdly, by integrating the optimization model with the SLSQP (Sequential Least Squares Quadratic Programming algorithm), the second-order sequential quadratic programming model and decision variable update equations are derived, establishing the solution process for the trajectory planning problem of the tilt-wing eVTOL takeoff with obstacle avoidance. Finally, the Airbus Vahana A3 is taken as an example to verify and validate the effectiveness, stability, and robustness of the model and optimization algorithm proposed. The validation results show that the OTPEM (obstacle avoidance trajectory planning methodology based on energy minimization) can effectively handle changes in the takeoff end state and exhibits good stability and robustness in different obstacle environments. It can provide a certain reference for the three-dimensional obstacle avoidance trajectory planning of Airbus Vahana A3 and other tilt-wing eVTOL trajectory planning problems.

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.

Integrated scheduling of multi-constraint open shop and vehicle routing: Mathematical model and learning-driven brain storm optimization algorithm

Recent years have witnessed a surge of interest in integrated production and distribution scheduling problems which can achieve an overall optimization of the production and distribution activities. However, integrated scheduling of open shop and distribution receives rare attention in existing studies. This work proposes an integrated scheduling problem of multi-constraint open shop and vehicle routing to minimize maximum completion time, where group and transportation operations are considered together in the production process. All jobs are divided into multiple groups, and then handled in an open shop with multiple machines. Subsequently, the jobs are delivered to their corresponding customers. First, a mixed integer programming model is formulated to define the problem. Second, a Q-learning-driven brain storm optimization algorithm is developed to address the formulated model. A Q-learning method is employed to choose search strategies for generating new individuals rather than using fixed probability parameters straightforwardly as basic brain storm optimizers. In addition, the solution encoding, heuristic decoding, population initialization, clustering, new individual generation and selection methods are specially devised in consideration of problem-specific knowledge. At last, the developed model and algorithm are verified by addressing a set of benchmark instances, and comparison experiments are conducted with an exact solver CPLEX and four meta-heuristics from existing literature. The results validate the competitive advantages of the formulated model and algorithm in solving the considered problems.

Research on Variable Speed Variable Displacement Power Unit with High Efficiency and High Dynamic Optimized Matching

For the variable speed variable displacement power unit (VSVDPU), achieving power matching between the permanent magnet synchronous motor (PMSM) and the variable displacement plunger pump (VDPP) is the key to reducing system energy consumption. The control method of adjusting the speed of the PMSM and the displacement of the VDPP is the mainstay of current research and application of the VSVDPU. However, the dynamic properties of the PMSM and VDPP have not been balanced, which affects the control effect of the VSVDPU. This paper proposes a control method of variable speed and variable displacement with low energy consumption and high dynamics. The main idea is based on the efficiency model and dynamic response model of the PMSM and VDPP, and the factors that affect the efficiency and dynamic characteristics of the VSVDPU are analyzed. Guided by the multi-objective optimization algorithm, the optimal combination of speed and displacement under specific working conditions is derived. Simulation and experiment results show that the proposed control method is feasible to improve the efficiency and dynamic characteristics of the VSVDPU.