Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow an...This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.展开更多
Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper propose...Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm(IChOA).First,this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints,transforming the path planning problem into an optimization problem with multiple constraints.Second,this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed.Finally,this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons,which effectively balances global exploration and local exploitation,significantly optimizing the algorithm’s search performance.To validate the effectiveness of the IChOA algorithm,this paper conducts experimental comparisons with eight different intelligent algorithms.The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning.It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.展开更多
In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in ...In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.展开更多
As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the mo...As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the most suitable functional type,while beset by various uncertainties.Weight uncertainties,being affected by subjective preferences,are highly arbitrary and seriously affect PLES.Taking Xuzhou as the study area,this paper studies the perturbation mechanism and response measure of weight uncertainties on PLES.First,weight samples are obtained through quasi-random sampling to serve as sources of uncertainties for input into the optimized delineation of PLES.Next,the Monte Carlo simulation is applied to simulate the spatial probability distribution of PLES.The global sensitivity analysis method is then adopted to identify the main sources that cause uncertainties in the delineation of PLES.Subsequently,the flexible space(FS)of PLES at a certain level of significance is formulated by comparing the distribution probabilities of spatial units for different functional spaces,acting as a countermeasure for the perturbation.The results show that weight uncertainties bring disturbances to the PLES by affecting the multi-criteria evaluation(MCE)of PLES delineation.The PLES is affected by the weight uncertainties of the factors alone or through interactions with other weights.FS is the spatial response measure of PLES when uncertainties occurred at a certain level of significance.The study introduces the perspective of uncertainty for PLES,which contributes toward improving the scientificity and reliability of PLES.展开更多
Under the background of this era,green finance and the upgrading and optimization of industrial structure have become a hot research topic.The article focuses on Jiangsu Province,carefully explores the impact of green...Under the background of this era,green finance and the upgrading and optimization of industrial structure have become a hot research topic.The article focuses on Jiangsu Province,carefully explores the impact of green financial development on the upgrading and optimization of industrial structure and the real effect,collates and summarizes the theories of green finance and industrial structure at home and abroad,and carefully analyzes the development of green finance in Jiangsu Province,such as the gradual expansion of green credit scale,the characteristics of industrial structure,the change of the proportion of three industries,the development situation of emerging industries and so on.By means of econometrics,an empirical model covering Green Financial Development Indicators and industrial structure optimization indicators is established to do multiple linear regression analysis and stability test.The empirical results show that the development of green finance in Jiangsu plays an obvious positive role in the optimization and upgrading of industrial structure.Green finance is environmental protection,new energy and other green industries are given important financial support,which drives their scale expansion and technological innovation,and makes the industrial structure develop towards a higher level and a more reasonable direction.From this point of view,corresponding proposals are put forward to improve the policy incentive system,add green financial products,and strengthen the construction of green financial market.The purpose is to give better play to the advantages of green finance,accelerate the optimization and upgrading of industrial structure in Jiangsu,and provide theoretical basis and practical guidance for achieving green economic transformation and sustainable development.展开更多
Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation indust...Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.展开更多
The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the intera...The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.展开更多
This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenanc...This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency.RSWTLBO combines adaptive parameter w,Single Solution Optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of TLBO.The algorithm achieves a balance between exploitation and exploration throughout the iteration process.The SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior solutions.The WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local optima.The algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV models.The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module models.It also exhibits robust performance under varying irradiation and temperature conditions.The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absol...A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.展开更多
Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori...Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.展开更多
A light?weight design method of integrated structural topology and size co?optimization for the force?performance?structure of complex structural parts is presented in this paper. Firstly, the supporting function of a...A light?weight design method of integrated structural topology and size co?optimization for the force?performance?structure of complex structural parts is presented in this paper. Firstly, the supporting function of a complex structural part is built to map the force transmission, where the force exerted areas and constraints are considered as connecting structure and the structural configuration, to determine the part performance as well as the force routines. Then the connecting structure design model, aiming to optimize the static and dynamic performances on connection configuration, is developed, and the optimum design of the characteristic parameters is carried out by means of the collaborative optimization method, namely, the integrated structural topology optimization and size optimization. In this design model, the objective is to maximize the connecting stiffness. Based on the relationship between the force and the structural configuration of a part, the optimal force transmission routine that can meet the performance requirements is obtained using the structural topology optimization technology. Accordingly, the light?weight design of conceptual configuration for complex parts under multi?objective and multi?condition can be realized. Finally, based on the proposed collaborative optimization design method, the optimal performance and optimal structure of the complex parts with light weight are realized, and the reasonable structural unit configuration and size charac?teristic parameters are obtained. A bed structure of gantry?type machining center is designed by using the proposed light?weight structure design method in this paper, as an illustrative example. The bed after the design optimization is lighter 8% than original one, and the rail deformation is reduced by 5%. Moreover, the lightweight design of the bed is achieved with enhanced performance to show the effectiveness of the proposed method.展开更多
Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired ...Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.展开更多
Stress-based topology optimization is one of the most concerns of structural optimization and receives much attention in a wide range of engineering designs.To solve the inherent issues of stress-based topology optimi...Stress-based topology optimization is one of the most concerns of structural optimization and receives much attention in a wide range of engineering designs.To solve the inherent issues of stress-based topology optimization,many schemes are added to the conventional bi-directional evolutionary structural optimization(BESO)method in the previous studies.However,these schemes degrade the generality of BESO and increase the computational cost.This study proposes an improved topology optimization method for the continuum structures considering stress minimization in the framework of the conventional BESO method.A global stress measure constructed by p-norm function is treated as the objective function.To stabilize the optimization process,both qp-relaxation and sensitivity weight scheme are introduced.Design variables are updated by the conventional BESO method.Several 2D and 3D examples are used to demonstrate the validity of the proposed method.The results show that the optimization process can be stabilized by qp-relaxation.The value of q and p are crucial to reasonable solutions.The proposed sensitivity weight scheme further stabilizes the optimization process and evenly distributes the stress field.The computational efficiency of the proposed method is higher than the previous methods because it keeps the generality of BESO and does not need additional schemes.展开更多
The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A a...The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.展开更多
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta...The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.展开更多
This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design...This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Applicable Eurocode 3 design conditions are presented, which provide the constraints for the problem. Steel truss is a preferred solution in large-span roof structures due to its good attributes, such as being lightweight and durable. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Constant spans of the trusses have been considered, and each truss has been subjected to the same types of load cases. The top chord member load has been kept constant in each truss at 2 kN/m. Two sets of load conditions are taken as the self-weight of the truss and the snow load, but the structure is calculated by the load combination. The structural steel trusses were optimized using the design optimization tool as a first-order optimization method in RFEM, and it was extended to compare the most suitable truss geometry for the minimum weight. Finally, it is concluded that the Warren truss has a higher stiffness-to-weight ratio than other trusses after optimization. The goal of this study was to analyze all trusses and ensure that the structural stress is less than the allowable stress and that the deflection is less than the allowable deflection. The span and height are constant in all cases because they have no impact on the weight increase;only the position of the rods and cross-section size affect the building’s ability to withstand loads and weight increases. In this paper, a finite element analysis (FEA)-based optimization technique is proposed for the optimization of a light design that is constrained by allowable stresses and deflections. For this purpose, there have been studies on sizing optimization to minimize the mass of different steel truss roof system types both in the past and today. For this purpose, weight design and analysis of the optimum weight are carried out on ten different structural systems.展开更多
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
文摘This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.
基金supported by the Shaanxi Province Natural Science Basic Research Program Project(2024JC-YBMS-572)partially funded by Yan’an University Graduate Education Innovation Program Project(YCX2023032,YCX2023033,YCX2024094,YCX2024097)the“14th Five Year Plan Medium and Long Term Major Scientific Research Project”(2021ZCQ015)of Yan’an University.
文摘Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm(IChOA).First,this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints,transforming the path planning problem into an optimization problem with multiple constraints.Second,this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed.Finally,this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons,which effectively balances global exploration and local exploitation,significantly optimizing the algorithm’s search performance.To validate the effectiveness of the IChOA algorithm,this paper conducts experimental comparisons with eight different intelligent algorithms.The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning.It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.
基金National Natural Science Foundation of China,grant number 62205120,funded this research.
文摘In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.
基金Under the auspices of National Natural Science Foundation of China(No.42171248,42371273)。
文摘As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the most suitable functional type,while beset by various uncertainties.Weight uncertainties,being affected by subjective preferences,are highly arbitrary and seriously affect PLES.Taking Xuzhou as the study area,this paper studies the perturbation mechanism and response measure of weight uncertainties on PLES.First,weight samples are obtained through quasi-random sampling to serve as sources of uncertainties for input into the optimized delineation of PLES.Next,the Monte Carlo simulation is applied to simulate the spatial probability distribution of PLES.The global sensitivity analysis method is then adopted to identify the main sources that cause uncertainties in the delineation of PLES.Subsequently,the flexible space(FS)of PLES at a certain level of significance is formulated by comparing the distribution probabilities of spatial units for different functional spaces,acting as a countermeasure for the perturbation.The results show that weight uncertainties bring disturbances to the PLES by affecting the multi-criteria evaluation(MCE)of PLES delineation.The PLES is affected by the weight uncertainties of the factors alone or through interactions with other weights.FS is the spatial response measure of PLES when uncertainties occurred at a certain level of significance.The study introduces the perspective of uncertainty for PLES,which contributes toward improving the scientificity and reliability of PLES.
基金The Impact of Digital Economy on Green Development Efficiency.2025 Nanjing University of Science and Technology Zijin College Campus Level Scientific Research Project(Project No.:2025ZXSK0401011)。
文摘Under the background of this era,green finance and the upgrading and optimization of industrial structure have become a hot research topic.The article focuses on Jiangsu Province,carefully explores the impact of green financial development on the upgrading and optimization of industrial structure and the real effect,collates and summarizes the theories of green finance and industrial structure at home and abroad,and carefully analyzes the development of green finance in Jiangsu Province,such as the gradual expansion of green credit scale,the characteristics of industrial structure,the change of the proportion of three industries,the development situation of emerging industries and so on.By means of econometrics,an empirical model covering Green Financial Development Indicators and industrial structure optimization indicators is established to do multiple linear regression analysis and stability test.The empirical results show that the development of green finance in Jiangsu plays an obvious positive role in the optimization and upgrading of industrial structure.Green finance is environmental protection,new energy and other green industries are given important financial support,which drives their scale expansion and technological innovation,and makes the industrial structure develop towards a higher level and a more reasonable direction.From this point of view,corresponding proposals are put forward to improve the policy incentive system,add green financial products,and strengthen the construction of green financial market.The purpose is to give better play to the advantages of green finance,accelerate the optimization and upgrading of industrial structure in Jiangsu,and provide theoretical basis and practical guidance for achieving green economic transformation and sustainable development.
基金supported in part by the National Key Research and Development Program of China(2021YFC2902703)the National Natural Science Foundation of China(62173078,61773105,61533007,61873049,61873053,61703085,61374147)。
文摘Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.
基金supported in part by the Central Government Guides Local Science and TechnologyDevelopment Funds(Grant No.YDZJSX2021A038)in part by theNational Natural Science Foundation of China under(Grant No.61806138)in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)(Grant 2021FNA04014).
文摘The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LTGS23E070001)National Natural Science Foundation of China(62076185,62301367).
文摘This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency.RSWTLBO combines adaptive parameter w,Single Solution Optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of TLBO.The algorithm achieves a balance between exploitation and exploration throughout the iteration process.The SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior solutions.The WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local optima.The algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV models.The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module models.It also exhibits robust performance under varying irradiation and temperature conditions.The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
基金The US National Science Foundation (No. CMMI-0408390,CMMI-0644552,BCS-0527508)the National Natural Science Foundation of China (No. 51010044,U1134206)+2 种基金the Fok YingTong Education Foundation (No. 114024)the Natural Science Foundation of Jiangsu Province (No. BK2009015)the Postdoctoral Science Foundation of Jiangsu Province (No. 0901005C)
文摘A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.
基金supported by the Foundation of the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province(Grant No.181RTSTHN009)the Foundation of the Key Laboratory of Water Environment Simulation and Treatment in Henan Province(Grant No.2017016).
文摘Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.
基金Supported by National Science and Technology Major Project(Grant No.2015ZX04014021)
文摘A light?weight design method of integrated structural topology and size co?optimization for the force?performance?structure of complex structural parts is presented in this paper. Firstly, the supporting function of a complex structural part is built to map the force transmission, where the force exerted areas and constraints are considered as connecting structure and the structural configuration, to determine the part performance as well as the force routines. Then the connecting structure design model, aiming to optimize the static and dynamic performances on connection configuration, is developed, and the optimum design of the characteristic parameters is carried out by means of the collaborative optimization method, namely, the integrated structural topology optimization and size optimization. In this design model, the objective is to maximize the connecting stiffness. Based on the relationship between the force and the structural configuration of a part, the optimal force transmission routine that can meet the performance requirements is obtained using the structural topology optimization technology. Accordingly, the light?weight design of conceptual configuration for complex parts under multi?objective and multi?condition can be realized. Finally, based on the proposed collaborative optimization design method, the optimal performance and optimal structure of the complex parts with light weight are realized, and the reasonable structural unit configuration and size charac?teristic parameters are obtained. A bed structure of gantry?type machining center is designed by using the proposed light?weight structure design method in this paper, as an illustrative example. The bed after the design optimization is lighter 8% than original one, and the rail deformation is reduced by 5%. Moreover, the lightweight design of the bed is achieved with enhanced performance to show the effectiveness of the proposed method.
基金support of the special project for collaborative innovation of science and technology in 2021(No:202121206)Henan Province University Scientific and Technological Innovation Team(No:18IRTSTHN009).
文摘Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.
基金supported by National Natural Science Foundation of China[Grant No.51575399]the National Key Research and Development Program of China[Grant No.2016YFB0101602].
文摘Stress-based topology optimization is one of the most concerns of structural optimization and receives much attention in a wide range of engineering designs.To solve the inherent issues of stress-based topology optimization,many schemes are added to the conventional bi-directional evolutionary structural optimization(BESO)method in the previous studies.However,these schemes degrade the generality of BESO and increase the computational cost.This study proposes an improved topology optimization method for the continuum structures considering stress minimization in the framework of the conventional BESO method.A global stress measure constructed by p-norm function is treated as the objective function.To stabilize the optimization process,both qp-relaxation and sensitivity weight scheme are introduced.Design variables are updated by the conventional BESO method.Several 2D and 3D examples are used to demonstrate the validity of the proposed method.The results show that the optimization process can be stabilized by qp-relaxation.The value of q and p are crucial to reasonable solutions.The proposed sensitivity weight scheme further stabilizes the optimization process and evenly distributes the stress field.The computational efficiency of the proposed method is higher than the previous methods because it keeps the generality of BESO and does not need additional schemes.
基金supported by the National Natural Science Foundation of China(Nos.62265010,62061024)Gansu Province Science and Technology Plan(No.23YFGA0062)Gansu Province Innovation Fund(No.2022A-215)。
文摘The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.
基金Project of Key Science and Technology of the Henan Province(No.202102310259)Henan Province University Scientific and Technological Innovation Team(No.18IRTSTHN009).
文摘The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.
文摘This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Applicable Eurocode 3 design conditions are presented, which provide the constraints for the problem. Steel truss is a preferred solution in large-span roof structures due to its good attributes, such as being lightweight and durable. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Constant spans of the trusses have been considered, and each truss has been subjected to the same types of load cases. The top chord member load has been kept constant in each truss at 2 kN/m. Two sets of load conditions are taken as the self-weight of the truss and the snow load, but the structure is calculated by the load combination. The structural steel trusses were optimized using the design optimization tool as a first-order optimization method in RFEM, and it was extended to compare the most suitable truss geometry for the minimum weight. Finally, it is concluded that the Warren truss has a higher stiffness-to-weight ratio than other trusses after optimization. The goal of this study was to analyze all trusses and ensure that the structural stress is less than the allowable stress and that the deflection is less than the allowable deflection. The span and height are constant in all cases because they have no impact on the weight increase;only the position of the rods and cross-section size affect the building’s ability to withstand loads and weight increases. In this paper, a finite element analysis (FEA)-based optimization technique is proposed for the optimization of a light design that is constrained by allowable stresses and deflections. For this purpose, there have been studies on sizing optimization to minimize the mass of different steel truss roof system types both in the past and today. For this purpose, weight design and analysis of the optimum weight are carried out on ten different structural systems.
