In Iran,the intensity of energy consumption in the building sector is almost 3 times the world average,and due to the consumption of fossil fuels as the main source of energy in this sector,as well as the lack of opti...In Iran,the intensity of energy consumption in the building sector is almost 3 times the world average,and due to the consumption of fossil fuels as the main source of energy in this sector,as well as the lack of optimal design of buildings,it has led to excessive release of toxic gases into the environment.This research develops an efficient approach for the simulation-oriented Pareto optimization(SOPO)of building energy efficiency to assist engineers in optimal building design in early design phases.To this end,EnergyPlus,as one of the most powerful and well-known whole-building simulation programs,is combined with the Multi-objective Ant Colony Optimization(MOACO)algorithm through the JAVA programming language.As a result,the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties.To evaluate the effectiveness of the developed method,it was performed on a residential building located in the hot and semi-arid region of Iran.To obtain the optimum configuration of the building under investigation,the building rotation,window-to-wall ratio,tilt angle of shading device,depth of shading device,color of the external walls,area of solar collector,tilt angle of solar collector,rotation of solar collector,cooling and heating setpoints of heating,ventilation,and air conditioning(HVAC)system are chosen as decision variables.Further,the building energy consumption(BEC),solar collector efficiency(SCE),and predicted percentage of dissatisfied(PPD)index as a measure of the occupants'thermal comfort level are chosen as the objective functions.The single-objective optimization(SO)and Pareto optimization(PO)are performed.The obtained results are compared to the initial values of the basic model.The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs,owing to the lower value of the deviation index.Moreover,the optimal solutions extracted through the PO are depicted in the form of Pareto fronts.Eventually,the Linear Programming Technique for Multidimensional Analysis of Preference(LINMAP)technique as one of the well-known multi-criteria decision-making(MCDM)methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions.Further,the results of PO show that although BEC increases from 136 GJ to 140 GJ,PPD significantly decreases from 26%to 8%and SCE significantly increases from 16%to 25%.The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making.In addition,the method presented in this study can be applied to different types of buildings in different climates.展开更多
This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration s...This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.展开更多
Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The Pareto optimal front...Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The Pareto optimal front is obtained in closed-form, enabling the derivation of various solutions in a convenient and efficient way. The advantage of analytical solution is the possibility of deriving accurate, exact and well-understood solutions, which is especially useful for policy analysis. An extension of the method to include multiple objectives is provided with the objectives being classified into two types. Such an extension expands the applicability of the developed techniques.展开更多
For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ...For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.展开更多
Except for the bad weather or other uncontrollable reasons,a reasonable queue of departure and arrival flights is one of the important methods to reduce the delay on busy airports.Here focusing on the Pareto optimizat...Except for the bad weather or other uncontrollable reasons,a reasonable queue of departure and arrival flights is one of the important methods to reduce the delay on busy airports.Here focusing on the Pareto optimization of departure flights,the take-off sequencing is taken as a single machine scheduling problem with two objective functions,i.e.,the minimum of total weighted delayed number of departure flights and the latest delay time of delayed flight.And the integer programming model is established and solved by multi-objective genetic algorithm.The simulation results show that the method can obtain the better goal,and provide a variety of options for controllers considering the scene situation,thus improving the flexibility and effectivity of flight plan.展开更多
The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental ...The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental design （L9 orthogonal array） method, a series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed were considered responses. Based on the signal-to-noise （S/N） ratio, the influence of the input parameters on the responses was determined. The optimal machining parameters setting for the maximum cutting speed and minimum surface roughness were found using Taguchi methodology. Then, additive model was employed for prediction of all （34） possible machining combinations. Finally, a handy technology table has been reported using Pareto optimality approach.展开更多
Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions ...Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.展开更多
This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platfo...This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.展开更多
The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive obj...The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity.展开更多
An optimization solution to roll shifting strategy for alternately rolling campaign is presented. All the strips in a rolling campaign are divided into narrow strips and wide ones, and shifting position of narrow stri...An optimization solution to roll shifting strategy for alternately rolling campaign is presented. All the strips in a rolling campaign are divided into narrow strips and wide ones, and shifting position of narrow strips is obtained by the recursive method, and then shifting position of wide strips is optimized by NSGA-II which is a multiobjective genetic algorithm. For wide strips, a multi-objective optimization model of roll shifting strategy is pro posed, which takes 3 wear contour factors including edge smoothness, body smoothness and edge drop as optimization objectives. The Pareto optimal front of roll shifting strategy can be gained quickly by NSGA-II, which suggests a series of alternative solutions to roll shifting strategy. Analysis shows that the conflict exists among the 3 objectives. The final optimal solution is selected from the Pareto optimal solutions by the weighted-sum decision-making method. Industrial production proves the validity of the solution, and it can improve strip profile of alternately rolling, reduce strip edge wave, and extend the rolling miles of rolling campaigns.展开更多
At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effect...At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.展开更多
To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description ab...To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.展开更多
Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective op...Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.展开更多
There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each gam...There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.展开更多
As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimizat...As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.展开更多
In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce ...In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.展开更多
A microbial fuel cell(MFC)is a novel promising technology for simultaneous renewable electricity generation and wastewater treatment.Three non-comparable objectives,i.e.power density,attainable current density and was...A microbial fuel cell(MFC)is a novel promising technology for simultaneous renewable electricity generation and wastewater treatment.Three non-comparable objectives,i.e.power density,attainable current density and waste removal ratio,are often conflicting.A thorough understanding of the relationship among these three conflicting objectives can be greatly helpful to assist in optimal operation of MFC system.In this study,a multiobjective genetic algorithm is used to simultaneously maximizing power density,attainable current density and waste removal ratio based on a mathematical model for an acetate two-chamber MFC.Moreover,the level diagrams method is utilized to aid in graphical visualization of Pareto front and decision making.Three biobjective optimization problems and one three-objective optimization problem are thoroughly investigated.The obtained Pareto fronts illustrate the complex relationships among these three objectives,which is helpful for final decision support.Therefore,the integrated methodology of a multi-objective genetic algorithm and a graphical visualization technique provides a promising tool for the optimal operation of MFCs by simultaneously considering multiple conflicting objectives.展开更多
A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set ...A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.展开更多
In Corley′s algorithm for all efficient spanning trees, final solutions include many spanning trees, which are not all efficient. In this paper, a new algorithm is presented, which corrects and modifies Corley′s alg...In Corley′s algorithm for all efficient spanning trees, final solutions include many spanning trees, which are not all efficient. In this paper, a new algorithm is presented, which corrects and modifies Corley′s algorithm. A necessary condition is developed for the subtree of an efficient spanning tree. According to the condition the new algorithm is established and its efficiency is proved.展开更多
文摘In Iran,the intensity of energy consumption in the building sector is almost 3 times the world average,and due to the consumption of fossil fuels as the main source of energy in this sector,as well as the lack of optimal design of buildings,it has led to excessive release of toxic gases into the environment.This research develops an efficient approach for the simulation-oriented Pareto optimization(SOPO)of building energy efficiency to assist engineers in optimal building design in early design phases.To this end,EnergyPlus,as one of the most powerful and well-known whole-building simulation programs,is combined with the Multi-objective Ant Colony Optimization(MOACO)algorithm through the JAVA programming language.As a result,the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties.To evaluate the effectiveness of the developed method,it was performed on a residential building located in the hot and semi-arid region of Iran.To obtain the optimum configuration of the building under investigation,the building rotation,window-to-wall ratio,tilt angle of shading device,depth of shading device,color of the external walls,area of solar collector,tilt angle of solar collector,rotation of solar collector,cooling and heating setpoints of heating,ventilation,and air conditioning(HVAC)system are chosen as decision variables.Further,the building energy consumption(BEC),solar collector efficiency(SCE),and predicted percentage of dissatisfied(PPD)index as a measure of the occupants'thermal comfort level are chosen as the objective functions.The single-objective optimization(SO)and Pareto optimization(PO)are performed.The obtained results are compared to the initial values of the basic model.The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs,owing to the lower value of the deviation index.Moreover,the optimal solutions extracted through the PO are depicted in the form of Pareto fronts.Eventually,the Linear Programming Technique for Multidimensional Analysis of Preference(LINMAP)technique as one of the well-known multi-criteria decision-making(MCDM)methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions.Further,the results of PO show that although BEC increases from 136 GJ to 140 GJ,PPD significantly decreases from 26%to 8%and SCE significantly increases from 16%to 25%.The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making.In addition,the method presented in this study can be applied to different types of buildings in different climates.
基金funded by the National Natural Science Foundation of China(12102487)Basic and Applied Basic Research Foundation of Guangdong Province,China(2023A1515012339)+1 种基金Shenzhen Science and Technology Program(ZDSYS20210623091808026)the Discovery Grant(RGPIN-2024-06290)of the Natural Sciences and Engineering Research Council of Canada。
文摘This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.
文摘Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The Pareto optimal front is obtained in closed-form, enabling the derivation of various solutions in a convenient and efficient way. The advantage of analytical solution is the possibility of deriving accurate, exact and well-understood solutions, which is especially useful for policy analysis. An extension of the method to include multiple objectives is provided with the objectives being classified into two types. Such an extension expands the applicability of the developed techniques.
基金National Science Fund for Distinguished Young Scholars (10425208)Programme of Introducing Talents of Discipline to Universities (B07009)
文摘For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.
基金supported by the National Natural Science Foundation of China(No.61079013)the Natural Science Fund Project in Jiangsu Province(No.BK2011737)
文摘Except for the bad weather or other uncontrollable reasons,a reasonable queue of departure and arrival flights is one of the important methods to reduce the delay on busy airports.Here focusing on the Pareto optimization of departure flights,the take-off sequencing is taken as a single machine scheduling problem with two objective functions,i.e.,the minimum of total weighted delayed number of departure flights and the latest delay time of delayed flight.And the integer programming model is established and solved by multi-objective genetic algorithm.The simulation results show that the method can obtain the better goal,and provide a variety of options for controllers considering the scene situation,thus improving the flexibility and effectivity of flight plan.
文摘The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental design （L9 orthogonal array） method, a series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed were considered responses. Based on the signal-to-noise （S/N） ratio, the influence of the input parameters on the responses was determined. The optimal machining parameters setting for the maximum cutting speed and minimum surface roughness were found using Taguchi methodology. Then, additive model was employed for prediction of all （34） possible machining combinations. Finally, a handy technology table has been reported using Pareto optimality approach.
基金partially supported by the National Natural Science Foundation of China(41930644,61972439)the Collaborative Innovation Project of Anhui Province(GXXT-2022-093)the Key Program in the Youth Elite Support Plan in Universities of Anhui Province(gxyqZD2019010)。
文摘Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.
基金financially supported by the National Natural Science Foundation of China(Grant No.52371261)the Science and Technology Projects of Liaoning Province(Grant No.2023011352-JH1/110).
文摘This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.
基金Supported by the National Natural Science Foundation of China （No.60421002）
文摘The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity.
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘An optimization solution to roll shifting strategy for alternately rolling campaign is presented. All the strips in a rolling campaign are divided into narrow strips and wide ones, and shifting position of narrow strips is obtained by the recursive method, and then shifting position of wide strips is optimized by NSGA-II which is a multiobjective genetic algorithm. For wide strips, a multi-objective optimization model of roll shifting strategy is pro posed, which takes 3 wear contour factors including edge smoothness, body smoothness and edge drop as optimization objectives. The Pareto optimal front of roll shifting strategy can be gained quickly by NSGA-II, which suggests a series of alternative solutions to roll shifting strategy. Analysis shows that the conflict exists among the 3 objectives. The final optimal solution is selected from the Pareto optimal solutions by the weighted-sum decision-making method. Industrial production proves the validity of the solution, and it can improve strip profile of alternately rolling, reduce strip edge wave, and extend the rolling miles of rolling campaigns.
基金financially supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0906)National Natural Science Foundation of China(No.41901008 and No.61976046)+3 种基金National Key R&D Program of China(No.2017YFC1502504)the Fundamental Research Funds for the Central Universities(Grant No.2682018CX05)Beijing Municipal Science and Technology Project(Z191100001419015)financially supported by the China Scholarship Council。
文摘At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.
文摘To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.
基金supported by Zhejiang Provincial Natural Science Foundation of China (No.Y1090866)supported by Dan Simon and Dawei Du of Cleveland State University, and Jeff Abell of General Motors, whose ideas were instrumental in the development of this research
文摘Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.
基金The project supported by the National Natural Science Foundation of China (10372040)Scientific Research Foundation (SRF) for Returned Oversea's Chinese Scholars (ROCS) (2003-091). The English text was polished by Yunming Chen
文摘There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.
基金supported by the National Natural Science Foundation of China(60873099)the Fundamental Research Funds for the Central Universities(2011QNA29)
文摘As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.
基金supported by the National Natural Science Foundation of China(No.60803049,60472060)
文摘In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.
基金Supported by the National Natural Science Foundation of China(21576163)the Major State Basic Research Development Program of China(2014CB239703)+1 种基金the Science and Technology Commission of Shanghai Municipality(14DZ2250800)the Project-sponsored by SRF for ROCS,SEM
文摘A microbial fuel cell(MFC)is a novel promising technology for simultaneous renewable electricity generation and wastewater treatment.Three non-comparable objectives,i.e.power density,attainable current density and waste removal ratio,are often conflicting.A thorough understanding of the relationship among these three conflicting objectives can be greatly helpful to assist in optimal operation of MFC system.In this study,a multiobjective genetic algorithm is used to simultaneously maximizing power density,attainable current density and waste removal ratio based on a mathematical model for an acetate two-chamber MFC.Moreover,the level diagrams method is utilized to aid in graphical visualization of Pareto front and decision making.Three biobjective optimization problems and one three-objective optimization problem are thoroughly investigated.The obtained Pareto fronts illustrate the complex relationships among these three objectives,which is helpful for final decision support.Therefore,the integrated methodology of a multi-objective genetic algorithm and a graphical visualization technique provides a promising tool for the optimal operation of MFCs by simultaneously considering multiple conflicting objectives.
基金National Natural Science Foundation ofChina( No.90 2 0 5 0 0 6) and Shanghai Rising Star Program( No.0 2 QG14 0 3 1)
文摘A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.
文摘In Corley′s algorithm for all efficient spanning trees, final solutions include many spanning trees, which are not all efficient. In this paper, a new algorithm is presented, which corrects and modifies Corley′s algorithm. A necessary condition is developed for the subtree of an efficient spanning tree. According to the condition the new algorithm is established and its efficiency is proved.
