The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa...The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.展开更多
In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural ne...In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.展开更多
The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response su...The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.展开更多
Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optic...Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.展开更多
Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating...Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently accurate.In this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm.The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case study.In the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,respectively.In the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated.The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study.Given that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model.展开更多
This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical...This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.展开更多
Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an im...Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II(NSGA-II)for solving the MOLA problem by integrating the patch-based,edge growing/decreasing,neighborhood,and constraint steering rules.By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30×30 grid,we find that:when compared to the classical NSGA-II,the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity;the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation;the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection.The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.展开更多
This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation...This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.展开更多
With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring....With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring. Task allocation for UAVs is the process of planning the division of work among UAVs, controlled from ground stations by human operators. This study formulates the UAV task-allocation problem as an extended traveling salesman problem and presents a novel UAV task-allocation model for complex air concentration monitoring tasks. Then, an optimized non-dominated sorting genetic algorithm III (NSGA-III) based on a twin-exclusion mechanism, hierarchical objective-domination operator, and segmented gene encoding (i.e., NSGA-III-TEHOD) is developed to solve complex task-allocation problems involving multiple UAVs, hierarchical objectives, obstacles, and ambient wind. The algorithm is tested in several simulations, and the results demonstrate that the new algorithm outperforms NSGA-III, non-dominated sorting genetic algorithm II (NSGA-II), and genetic algorithm (GA) in terms of efficiency of global convergence and early maturation prevention and is available for the hierarchical objective-optimization problems.展开更多
The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,th...The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.展开更多
Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ...Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.展开更多
基金Project supported by the National Basic Research Program of China (973 Program) (No. 2007CB714600)
文摘The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.
基金This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa University,Riyadh,Saudi Arabia.
文摘In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.
文摘The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.
文摘Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.
文摘Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently accurate.In this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm.The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case study.In the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,respectively.In the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated.The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study.Given that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model.
基金supported in part by the National Natural Science Foundation of China under Grant No.52177171 and 51877040Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment,Southeast University,China.
文摘This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.
文摘Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II(NSGA-II)for solving the MOLA problem by integrating the patch-based,edge growing/decreasing,neighborhood,and constraint steering rules.By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30×30 grid,we find that:when compared to the classical NSGA-II,the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity;the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation;the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection.The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.
文摘This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.
基金the National Key Research and Development Program of China (No. 2017YFC0209902)。
文摘With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring. Task allocation for UAVs is the process of planning the division of work among UAVs, controlled from ground stations by human operators. This study formulates the UAV task-allocation problem as an extended traveling salesman problem and presents a novel UAV task-allocation model for complex air concentration monitoring tasks. Then, an optimized non-dominated sorting genetic algorithm III (NSGA-III) based on a twin-exclusion mechanism, hierarchical objective-domination operator, and segmented gene encoding (i.e., NSGA-III-TEHOD) is developed to solve complex task-allocation problems involving multiple UAVs, hierarchical objectives, obstacles, and ambient wind. The algorithm is tested in several simulations, and the results demonstrate that the new algorithm outperforms NSGA-III, non-dominated sorting genetic algorithm II (NSGA-II), and genetic algorithm (GA) in terms of efficiency of global convergence and early maturation prevention and is available for the hierarchical objective-optimization problems.
基金supported in part by the Natural Science Foundation of Shandong Province(ZR2021QE289)in part by State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22201).
文摘The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
基金supported by the Natural Science Foundation of Shaanxi Province(2023-JC-QN-0728)the China Postdoctoral Science Foundation(2021M693942)。
文摘Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.
