To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved...To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.展开更多
The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj...The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.展开更多
With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for...With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.展开更多
Among CSP technologies,the linear Fresnel reflector(LFR)can provide reliable carbon-neutral electricity for large-scale applications.In this study,the performance of a large solar LFR power plant under varying climati...Among CSP technologies,the linear Fresnel reflector(LFR)can provide reliable carbon-neutral electricity for large-scale applications.In this study,the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications,such as solar multiple and full-load thermal storage hours,were examined.Next,artificial neural network(ANN)surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology.Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted.To overcome overfitting,validation and Bayesian Regularization approaches were compared.As training and testing data,36 geographical sites with various combinations of design parameters were used.Through multi-objective optimization techniques,including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling,this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria.The study also identified Site 4(S4)as a promising candidate for optimal balance between the capacity factor(51.05%)and specific cost(5246.71$/kW),showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.展开更多
The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem becau...The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search space.Moreover,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses.Different types ofMulti-Objective Optimization Algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared properly.This paper aims to employ and compare three commonly used MOOAs i.e.NSGA-II,MOPSO,and MOEA/D in urban land-use allocation problems.Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages.The objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment.Evaluation of results is based on the dispersion of the solutions,diversity of the solutions’space,and comparing the number of dominant solutions in Pareto-Fronts.The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses.However,the run time of NSGA-II is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest degree.Moreover,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution space.Furthermore,In terms of algorithm execution time,MOEA/D performed better than the other two.So,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.展开更多
Global warming,driven by human-induced disruptions to the natural carbon dioxide(CO_(2))cycle,is a pressing concern.To mitigate this,carbon capture and storage has emerged as a key strategy that enables the continued ...Global warming,driven by human-induced disruptions to the natural carbon dioxide(CO_(2))cycle,is a pressing concern.To mitigate this,carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources.Deep saline aquifers are of particular interest due to their substantial CO_(2) storage potential,often located near fossil fuel reservoirs.In this study,a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow.Due to the time-consuming nature of each realization of the numerical simulation,we introduce a sur-rogate aquifer model derived from extracted data.The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework.Unlike previous studies,which typically focused on single-parameter optimiza-tion,our research addresses this gap by performing multi-objective optimization for CO_(2) storage and breakthrough time in deep sa-line aquifers using a data-driven model.Our methodology encompasses preprocessing and feature selection,identifying eight pivotal parameters.Evaluation metrics include root mean square error(RMSE),mean absolute percentage error(MAPE)and R^(2).In predicting CO_(2) storage values,RMSE,MAPE and R^(2)in test data were 2.07%,1.52% and 0.99,respectively,while in blind data,they were 2.5%,2.05% and 0.99.For the CO_(2) breakthrough time,RMSE,MAPE and R^(2) in the test data were 2.1%,1.77% and 0.93,while in the blind data they were 2.8%,2.23% and 0.92,respectively.In addressing the substantial computational demands and time-consuming nature of coup-ling a numerical simulator with an optimization algorithm,we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm.Within this framework,we conducted 5000 comprehensive experi-ments to rigorously validate the development of the Pareto front,highlighting the depth of our computational approach.The findings of the study promise insights into the interplay between CO_(2) breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.展开更多
In the healthcare system,a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery.Selecting a surgical team is challenging for a multispecialty hospital as the pe...In the healthcare system,a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery.Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient care.The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them.In this paper,we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient.The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient’s subgroups.For this end,two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed.The proposed algorithms were tested using different datasets and benchmark functions.Furthermore,the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India.The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.展开更多
综合能源系统(integrated energy system,IES)是推进能源结构调整的关键平台,合理规划其设备配置能显著提高IES运行经济和系统稳定性。此外,由于可再生能源发电固有的随机性和间歇性以及负荷的峰谷特性,导致IES中多能耦合设备的输出波动...综合能源系统(integrated energy system,IES)是推进能源结构调整的关键平台,合理规划其设备配置能显著提高IES运行经济和系统稳定性。此外,由于可再生能源发电固有的随机性和间歇性以及负荷的峰谷特性,导致IES中多能耦合设备的输出波动,严重威胁IES的运行稳定性。为应对上述挑战,针对IES的经济和稳定运行,以混合储能系统配置成本,系统电压偏差以及净负荷波动最小化为目标,建立一个电-氢混合储能系统多目标优化规划模型。该模型在IEEE-33标准测试系统下,利用多目标人工蜂鸟算法(multi-objective artificial hummingbird algorithm,MOAHA)对电-氢混合储能系统的容量和位置进行优化规划。仿真结果表明,所提的优化规划方法能有效改善IES配电网络的电压分布和净负荷水平,同时凭借电-氢混合储能的互补特性使得IES的运行灵活性得到了提升。展开更多
With the rapid increase in solar photovoltaic(PV)installation capacity,the strain on grid transmission burden has intensified.A house energy management system is recognized as an effective solution to mitigate this gr...With the rapid increase in solar photovoltaic(PV)installation capacity,the strain on grid transmission burden has intensified.A house energy management system is recognized as an effective solution to mitigate this grid burden.However,existing research has not fully explored the potential of battery utilization and the forecasting of uncertainties.In this paper,a novel multi-objective optimization framework based on the genetic algorithm-based method for the house energy management system is proposed,to enhance renewable self-consumption,improve on-site renewable self-sufficiency,and optimize economic benefits for users.The framework integrates an artificial neural network for predictions of meteorological data and user load at a 5-minute temporal resolution,enabling the simulation and optimization of the PV-battery-flexible load system.Emphasizing deferrable loads,constant-temperature control loads,and batteries,the proposed framework devises optimal strategies for distributed PV battery systems in residential.It harnesses load flexibility and battery storage capabilities while incorporating comfort assessment metrics.This approach significantly improves the system’s economic and technical performance metrics,with system self-consumption rate,self-sufficiency rate,and cost reduction ratio improved by 13.5%,11.3%,and 6.2%,respectively,compared to the basic strategy.Additionally,the optimization of the air conditioning system enhances alignment with the photovoltaic generation,resulting in a 9.8%reduction in energy consumption and a 9.4%decrease in electricity costs,while maintaining user comfort at an acceptable level.The proposed framework promotes the practical application of renewable management systems,highlighting renewable energy efficient utilization,grid dependency reduction,and user economic benefit increase.展开更多
基金by National Natural Science Foundation of China(62373142,62033014)Natural Science Foundation of Hunan Province(2025JJ70017,2022JJ50074).
文摘To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of China
文摘The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
基金National Key R&D Program of China(Grant No.2019YFB1704600)National Natural Science Foundation of China(Grant Nos.51825502,51775216)Program for HUST Academic Frontier Youth Team of China(Grant No.2017QYTD04).
文摘With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.
文摘Among CSP technologies,the linear Fresnel reflector(LFR)can provide reliable carbon-neutral electricity for large-scale applications.In this study,the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications,such as solar multiple and full-load thermal storage hours,were examined.Next,artificial neural network(ANN)surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology.Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted.To overcome overfitting,validation and Bayesian Regularization approaches were compared.As training and testing data,36 geographical sites with various combinations of design parameters were used.Through multi-objective optimization techniques,including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling,this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria.The study also identified Site 4(S4)as a promising candidate for optimal balance between the capacity factor(51.05%)and specific cost(5246.71$/kW),showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.
文摘The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search space.Moreover,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses.Different types ofMulti-Objective Optimization Algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared properly.This paper aims to employ and compare three commonly used MOOAs i.e.NSGA-II,MOPSO,and MOEA/D in urban land-use allocation problems.Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages.The objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment.Evaluation of results is based on the dispersion of the solutions,diversity of the solutions’space,and comparing the number of dominant solutions in Pareto-Fronts.The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses.However,the run time of NSGA-II is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest degree.Moreover,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution space.Furthermore,In terms of algorithm execution time,MOEA/D performed better than the other two.So,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.
文摘Global warming,driven by human-induced disruptions to the natural carbon dioxide(CO_(2))cycle,is a pressing concern.To mitigate this,carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources.Deep saline aquifers are of particular interest due to their substantial CO_(2) storage potential,often located near fossil fuel reservoirs.In this study,a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow.Due to the time-consuming nature of each realization of the numerical simulation,we introduce a sur-rogate aquifer model derived from extracted data.The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework.Unlike previous studies,which typically focused on single-parameter optimiza-tion,our research addresses this gap by performing multi-objective optimization for CO_(2) storage and breakthrough time in deep sa-line aquifers using a data-driven model.Our methodology encompasses preprocessing and feature selection,identifying eight pivotal parameters.Evaluation metrics include root mean square error(RMSE),mean absolute percentage error(MAPE)and R^(2).In predicting CO_(2) storage values,RMSE,MAPE and R^(2)in test data were 2.07%,1.52% and 0.99,respectively,while in blind data,they were 2.5%,2.05% and 0.99.For the CO_(2) breakthrough time,RMSE,MAPE and R^(2) in the test data were 2.1%,1.77% and 0.93,while in the blind data they were 2.8%,2.23% and 0.92,respectively.In addressing the substantial computational demands and time-consuming nature of coup-ling a numerical simulator with an optimization algorithm,we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm.Within this framework,we conducted 5000 comprehensive experi-ments to rigorously validate the development of the Pareto front,highlighting the depth of our computational approach.The findings of the study promise insights into the interplay between CO_(2) breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.
文摘In the healthcare system,a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery.Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient care.The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them.In this paper,we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient.The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient’s subgroups.For this end,two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed.The proposed algorithms were tested using different datasets and benchmark functions.Furthermore,the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India.The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.
基金the financial support provided by the National Key Research and Development Program of China through the Grant No.2022YFB4200902.
文摘With the rapid increase in solar photovoltaic(PV)installation capacity,the strain on grid transmission burden has intensified.A house energy management system is recognized as an effective solution to mitigate this grid burden.However,existing research has not fully explored the potential of battery utilization and the forecasting of uncertainties.In this paper,a novel multi-objective optimization framework based on the genetic algorithm-based method for the house energy management system is proposed,to enhance renewable self-consumption,improve on-site renewable self-sufficiency,and optimize economic benefits for users.The framework integrates an artificial neural network for predictions of meteorological data and user load at a 5-minute temporal resolution,enabling the simulation and optimization of the PV-battery-flexible load system.Emphasizing deferrable loads,constant-temperature control loads,and batteries,the proposed framework devises optimal strategies for distributed PV battery systems in residential.It harnesses load flexibility and battery storage capabilities while incorporating comfort assessment metrics.This approach significantly improves the system’s economic and technical performance metrics,with system self-consumption rate,self-sufficiency rate,and cost reduction ratio improved by 13.5%,11.3%,and 6.2%,respectively,compared to the basic strategy.Additionally,the optimization of the air conditioning system enhances alignment with the photovoltaic generation,resulting in a 9.8%reduction in energy consumption and a 9.4%decrease in electricity costs,while maintaining user comfort at an acceptable level.The proposed framework promotes the practical application of renewable management systems,highlighting renewable energy efficient utilization,grid dependency reduction,and user economic benefit increase.
