With the continuous increase of rapid urbanization and population growth,sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers.Multi-objective land-...With the continuous increase of rapid urbanization and population growth,sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers.Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives.This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization(NSBBO)algorithm for solving the multi-objective land-use allocation problem,in which maximum accessibility,maximum compactness,and maximum spatial integration were formulated as spatial objectives;and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali,Rwanda.Efficient Non-dominated Sorting(ENS)algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions,and local search ability,and to accelerate the convergence speed of the algorithm.The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO.Furthermore,the proposed algorithm could generate optimal land use scenarios according to the preferred objectives,thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use.展开更多
Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present...Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.展开更多
X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hi...X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
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
This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison ...This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.展开更多
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to sol...Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by in-troduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated;this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.展开更多
Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when mode...Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.展开更多
This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of B...This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared quantitatively.From all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.展开更多
In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed...In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.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
This paper provides a new sorting algorithm called 'Only-Once-Sorting' algorithm a mathemati cal formula,this algorithm can put elements in the positions they should be stored only once,then compacts them.The ...This paper provides a new sorting algorithm called 'Only-Once-Sorting' algorithm a mathemati cal formula,this algorithm can put elements in the positions they should be stored only once,then compacts them.The algorithm completes sorting a sequence of n elements in a calculation time of O(n ).展开更多
Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is...Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is not very good and the computing method of relative score in Borda sorting algorithm is according to the rule of the linear regressive,but position relationship cannot fully represent the correlation changes.aimed at this drawback,the new sorting algorithm is proposed in this paper,named PMS-Sorting algorithm,firstly the position score of the returned results is standardized processing,and the similarity retrieval word string with the query results is combined into the algorithm,the similarity calculation method is also improved,through the experiment,the improved algorithm is superior to traditional sorting algorithm.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
By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting ...By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.展开更多
We apply the recent important result of serial sorting of n real numbers in time to the design of a parallel algorithm for sorting real numbers in time and operations. This is the first NC algorithm known to take oper...We apply the recent important result of serial sorting of n real numbers in time to the design of a parallel algorithm for sorting real numbers in time and operations. This is the first NC algorithm known to take operations for sorting real numbers.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
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ...The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.展开更多
基金supported by the Styrelsen för Internationellt Utvecklingssamarbete.
文摘With the continuous increase of rapid urbanization and population growth,sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers.Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives.This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization(NSBBO)algorithm for solving the multi-objective land-use allocation problem,in which maximum accessibility,maximum compactness,and maximum spatial integration were formulated as spatial objectives;and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali,Rwanda.Efficient Non-dominated Sorting(ENS)algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions,and local search ability,and to accelerate the convergence speed of the algorithm.The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO.Furthermore,the proposed algorithm could generate optimal land use scenarios according to the preferred objectives,thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use.
文摘Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.
基金supported by State Key Laboratory of Mineral Processing (No.BGRIMM-KJSKL-2022-16)China Postdoctoral Science Foundation (No.2021M700387)+1 种基金National Natural Science Foundation of China (No.G2021105015L)Ministry of Science and Technology of the People’s Republic of China (No.2022YFC2904502)。
文摘X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
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.
文摘This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.
基金the Natural Science Key Foundation of Heilongjiang Province of China (No. ZJG0503) China-UK Sci-ence Network from Royal Society UK
文摘Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by in-troduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated;this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.
基金supported by National Natural Science Foundation of China (No.60474059)Hi-tech Research and Development Program of China (863 Program,No.2006AA04Z160).
文摘Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.
文摘This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared quantitatively.From all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.
基金Foundation item： Projects（61102106, 61102105） supported by the National Natural Science Foundation of China Project（2013M530148） supported by China Postdoctoral Science Foundation Project（HEUCF120806） supported by the Fundamental Research Funds for the Central Universities of China
文摘In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.
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This paper provides a new sorting algorithm called 'Only-Once-Sorting' algorithm a mathemati cal formula,this algorithm can put elements in the positions they should be stored only once,then compacts them.The algorithm completes sorting a sequence of n elements in a calculation time of O(n ).
基金This work was funded by the National Natural Science Foundation of China under Grant(No.61772152 and No.61502037)the Basic Research Project(Nos.JCKY2016206B001,JCKY2014206C002 and JCKY2017604C010)the Technical Foundation Project(No.JSQB2017206C002).
文摘Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is not very good and the computing method of relative score in Borda sorting algorithm is according to the rule of the linear regressive,but position relationship cannot fully represent the correlation changes.aimed at this drawback,the new sorting algorithm is proposed in this paper,named PMS-Sorting algorithm,firstly the position score of the returned results is standardized processing,and the similarity retrieval word string with the query results is combined into the algorithm,the similarity calculation method is also improved,through the experiment,the improved algorithm is superior to traditional sorting algorithm.
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.
文摘By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.
文摘We apply the recent important result of serial sorting of n real numbers in time to the design of a parallel algorithm for sorting real numbers in time and operations. This is the first NC algorithm known to take operations for sorting real numbers.
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.
基金in part supported by the Key Research and Development Project of Hubei Province(Nos.2020BAB1141,2023BAB094)the Key Project of Science and Technology Research ProgramofHubei Educational Committee(No.D20211402)+1 种基金the Teaching Research Project of Hubei University of Technology(No.XIAO2018001)the Project of Xiangyang Industrial Research Institute of Hubei University of Technology(No.XYYJ2022C04).
文摘The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.
