Leaf functional traits are adaptations that enable plants to live under various environmental conditions. This study aims to determine the differences in leaf functional traits among plants grouped by growth habit, le...Leaf functional traits are adaptations that enable plants to live under various environmental conditions. This study aims to determine the differences in leaf functional traits among plants grouped by growth habit, leaf life span,leaf lifestyle, leaf form, and origin. Specific leaf area（SLA） of perennial or evergreen species was lower than that of annual or deciduous species because longer-lived leaves of perennial or evergreen species require more investment in structural integrity and/or defense against disturbances, especially with any resource constraint. SLA of large individuals was lower than that of small individuals. The low SLA in large individuals can improve their response to changing light and water conditions because increasing plant height is advantageous for light competition, but it can also impose a cost in terms of structural support and water transport. Petioles of plants with compound leaves were significantly longer than those of simple leaves because branching is expensive in terms of gaining height. SLA of plants increased with increasing invasiveness accordingly, and SLA of invasive plants was higher than that of their native congeners because invasive plants should invest more biomass on leaf growth rather than leaf structures per unit area to achieve a higher growth rate.Overall, variation in leaf functional traits among different groups may play an adaptive role in the successful survival of plants under diverse environments because leaf functional traits can lead to pronounced effects on leaf function,especially the acquisition and use of light. Plant species with different growth and leaf traits balance resource acquisition and leaf construction to minimize trade-offs and achieve fitness advantages in their natural habitat.展开更多
Differential Evolution （DE） has been well accepted ever, it usually involves a large number of fitness evaluations to as an effective evolutionary optimization technique. Howobtain a satisfactory solution. This disa...Differential Evolution （DE） has been well accepted ever, it usually involves a large number of fitness evaluations to as an effective evolutionary optimization technique. Howobtain a satisfactory solution. This disadvantage severely restricts its application to computationally expensive problems, for which a single fitness evaluation can be highly timeconsuming. In the past decade, a lot of investigations have been conducted to incorporate a surrogate model into an evolutionary algorithm （EA） to alleviate its computational burden in this scenario. However, only limited work was devoted to DE. More importantly, although various types of surrogate models, such as regression, ranking, and classification models, have been investigated separately, none of them consistently outperforms others. In this paper, we propose to construct a surrogate model by combining both regression and classification techniques. It is shown that due to the specific selection strategy of DE, a synergy can be established between these two types of models, and leads to a surrogate model that is more appropriate for DE. A novel surrogate model-assisted DE, named Classification- and Regression-Assisted DE （CRADE） is proposed on this basis. Experimental studies are carried out on a set of 16 benchmark functions, and CRADE has shown significant superiority over DE-assisted with only regression or classification models. Further comparison to three state-of-the-art DE variants, i.e., DE with global and local neighborhoods （DECL）, JADE, and composite DE （CODE）, also demonstrates the superiority of CRADE.展开更多
The paper deals with the application of multiwall carbon nanotubes(CNTs) to the adsorption of dyes from wastewater. Textile dyes are dangerous and diffused pollutant in wastewater, and the paper results confirmed the ...The paper deals with the application of multiwall carbon nanotubes(CNTs) to the adsorption of dyes from wastewater. Textile dyes are dangerous and diffused pollutant in wastewater, and the paper results confirmed the good adsorption ability of CNTs, with respect to classic active carbon, even for different dye types. The effect of surface treatments of CNTs was primarily investigated, revealing that neither the presence of residual catalyst nor common surface treatment(oxidation) affects the CNT's performances. Therefore less expensive nonpurified CNTs were assessed as good and economically convenient alternative for the process. In order to gain in generality in adsorption kinetic modelling, the parameters of the "best fitting" pseudo-second order model have been correlated to the main process variables(the dye initial concentration and the specific mass of CNTs.) setting-up a predictive kinetic model useful design new application of these materials in currently operating industrial operations for adsorption. In addition, isothermal data were used to screen all the relevant adsorption isotherms models and the Temkin model was confirmed as the more effective to accurately fit equilibrium data for any of the considered different dye types.展开更多
Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for t...Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation(EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applicatio展开更多
Mandy:Wow!Your mobile phone is so cool!Dan:Thanks,it's new.My parents bought it for me.Mandy:My parents would never buy me an expensive phone.You're so lucky.Dan:Maybe I am,maybe I'm not.Mandy:Why do you s...Mandy:Wow!Your mobile phone is so cool!Dan:Thanks,it's new.My parents bought it for me.Mandy:My parents would never buy me an expensive phone.You're so lucky.Dan:Maybe I am,maybe I'm not.Mandy:Why do you say that?Dan:Because of my phone,I broke my arm.展开更多
To address the challenges of high-dimensional constrained optimization problems with expensive simulation models,a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling(SADE-MLS)is proposed....To address the challenges of high-dimensional constrained optimization problems with expensive simulation models,a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling(SADE-MLS)is proposed.In SADE-MLS,differential evolution operators are executed to generate numerous high-dimensional candidate points.To alleviate the curse of dimensionality,a Manifold Learning-based Sampling(MLS)mechanism is developed to explore the high-dimensional design space effectively.In MLS,the intrinsic dimensionality of the candidate points is determined by a maximum likelihood estimator.Then,the candidate points are mapped into a low-dimensional space using the dimensionality reduction technique,which can avoid significant information loss during dimensionality reduction.Thus,Kriging surrogates are constructed in the low-dimensional space to predict the responses of the mapped candidate points.The candidate points with high constrained expected improvement values are selected for global exploration.Moreover,the local search process assisted by radial basis function and differential evolution is performed to exploit the design space efficiently.Several numerical benchmarks are tested to compare SADE-MLS with other algorithms.Finally,SADE-MLS is successfully applied to a solid rocket motor multidisciplinary optimization problem and a re-entry vehicle aerodynamic optimization problem,with the total impulse and lift to drag ratio being increased by 32.7%and 35.5%,respec-tively.The optimization results demonstrate the practicality and effectiveness of the proposed method in real engineering practices.展开更多
Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges,...Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.展开更多
The design of complex aerospace systems is a multidisciplinary design optimization(MDO)problem involving the interaction of multiple disciplines.However,because of the necessity of evaluating expensive black-box simul...The design of complex aerospace systems is a multidisciplinary design optimization(MDO)problem involving the interaction of multiple disciplines.However,because of the necessity of evaluating expensive black-box simulations,the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice.To resolve this,metamodel-based design optimization techniques have been applied to MDO.With these methods,system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency.This paper presents an overall survey of metamodel-based MDO for aerospace systems.From the perspective of aerospace system design,this paper introduces the fundamental methodology and technology of metamodel-based MDO,including aerospace system MDO problem formulation,metamodeling techniques,state-of-the-art metamodel-based multidisciplinary optimization strategies,and expensive black-box constraint-handling mechanisms.Moreover,various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering.The conclusions derived from this work are summarized in the final section of the paper.The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.展开更多
Medical education is associated with significant costs[1].These costs have led to a growing interest in how to deliver high quality or high quantity education on a limited budget.This in turn has led to an interest in...Medical education is associated with significant costs[1].These costs have led to a growing interest in how to deliver high quality or high quantity education on a limited budget.This in turn has led to an interest in how best to measure quantity,quality and cost and how to track these variables over time.The ultimate aim of the interest in cost and value in medical education is to展开更多
In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive p...In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive points,with two different search strategies respectively applied inside and outside the promising region.Besides,the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems.Tested upon a serial of benchmark math functions,the HMDSD method shows great efficiency and search accuracy.On top of that,a practical lightweight design demonstrates its superior performance.展开更多
In recent years there has been increasing demand for natural non-nutritive high intensity sweeteners with low-calorie value as an alternative to sucrose. Extracts of the leaves of Stevia rebaudiana (Bertoni), have bee...In recent years there has been increasing demand for natural non-nutritive high intensity sweeteners with low-calorie value as an alternative to sucrose. Extracts of the leaves of Stevia rebaudiana (Bertoni), have been known for their sweet taste. Steviosides and rebaudioside-A are the two major diterpenoid glucosides components present in the leaf extracts of the stevia, these glycosides are 300 times sweeter than sugar and also exhibits wide therapeutic activity. The conventional methods of isolation of steviosides involve long extraction and purification procedures;therefore optimization of product yields is a challenging problem. The present study, establishes a new improvised process of extraction of steviosides from the stevia leaves in which the dry treated leaves were grounded, defatted, and extracted through pressurized hot water extractor (PHWE), followed by purification and concentration of the sweet glycosides through ultra (UF) and nano (NF) membrane filtration in obtaining high (98.2%) purity steviosides. This process established “green” method for isolation of high quality steviol glycosides, with improved final yield is 10.1% from 11% of crude leaf extract and observed the improved organoleptic and biological activity (antioxidant). Thus the method confirms a simple, inexpensive and eco-friendly process in obtaining pure steviosides.展开更多
Saffron is the most precious and expensive agricultural product. A dehydration treatment is necessary to convert Crocus sativus L. stigmas into saffron spice. To the best of our knowledge, no information on mass trans...Saffron is the most precious and expensive agricultural product. A dehydration treatment is necessary to convert Crocus sativus L. stigmas into saffron spice. To the best of our knowledge, no information on mass transfer parameters of saffron stigmas is available in the literature. This study aimed at investigating the moisture transfer parameters and quality attributes of saffron stigmas under infrared treatment at different temperatures（60,70, …, 110 ℃）. It was observed that the dehydration process of the samples occurred in a short accelerating rate period at the start followed by a falling rate period. The effective moisture diffusivity and convective mass transfer coefficient were determined by using the Dincer and Dost model. The diffusivity values varied from1.1103 × 10^-10m^2·s^-1to 4.1397 × 10^-10m^2·s^-1 and mass transfer coefficient varied in the range of 2.6433 × 10^-7–8.7203 × 10^-7m·s^-1. The activation energy was obtained to be 27.86 kJ·mol^-1. The quality assessment results showed that the total crocin content increased, when the temperature increased up to90 ℃ but, in higher temperatures, the amount of crocin decreased slightly. The total safranal content of the samples decreased slightly when drying temperature increased from 60 ℃ to 70 ℃ and then continuously increased up to 110 ℃. Also, the amount of picrocrocin increased from 83.1 to 93.3 as the drying temperature increased from 60 ℃ to 100 ℃.展开更多
This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informat...This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction tool.The search operation conducted in this low space facilitates the population with fast convergence towards the optima.To strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process.Also,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed.The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200.As indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer.Compared with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.展开更多
INTRODUCTION Campus heating and cooling systems present a particular challenge to the incorporation of advances in technology to improve efficiency.An older system is generally more expensive to operate than a newer o...INTRODUCTION Campus heating and cooling systems present a particular challenge to the incorporation of advances in technology to improve efficiency.An older system is generally more expensive to operate than a newer one would be,but extensive upgrades are often difficult.Lengthy shutdowns affect multiple buildings,and new central heating and cooling equipment is very expensive.Buildings added over the years incorporate various types of systems,affecting central plant operation.Buried distribution piping is costly and can be disruptive to modify.展开更多
The prediction method of dynamic wavelength is proposed for temperature tuning process. The temperature of the thermistor integrated in laser diode(LD) module is recorded to predict the LD chip temperature. Then accor...The prediction method of dynamic wavelength is proposed for temperature tuning process. The temperature of the thermistor integrated in laser diode(LD) module is recorded to predict the LD chip temperature. Then according to the injection current and priori tuning characteristics of the LDs, the emission wavelength is estimated in real time. The method is validated by using a 1.58 μm distributed feedback(DFB) LD. The absorption spectra of mixture gas of CO_2 and CO are measured by means of the thermal tuning gas sensing system. The center wavelength of each absorption line is compared with the data in HITRAN2012 database. The results show that the deviations are less than 5 pm. This method fully meets the needs of spectroscopic measurement, and can be applied to spectroscopy, optical communications and other fields.展开更多
基金supported by the National Natural Science Foundation of China(31300343)Natural Science Foundation of Jiangsu Province,China(BK20130500)Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment,Research Foundation for Advanced Talents,Jiangsu University(12JDG086)
文摘Leaf functional traits are adaptations that enable plants to live under various environmental conditions. This study aims to determine the differences in leaf functional traits among plants grouped by growth habit, leaf life span,leaf lifestyle, leaf form, and origin. Specific leaf area（SLA） of perennial or evergreen species was lower than that of annual or deciduous species because longer-lived leaves of perennial or evergreen species require more investment in structural integrity and/or defense against disturbances, especially with any resource constraint. SLA of large individuals was lower than that of small individuals. The low SLA in large individuals can improve their response to changing light and water conditions because increasing plant height is advantageous for light competition, but it can also impose a cost in terms of structural support and water transport. Petioles of plants with compound leaves were significantly longer than those of simple leaves because branching is expensive in terms of gaining height. SLA of plants increased with increasing invasiveness accordingly, and SLA of invasive plants was higher than that of their native congeners because invasive plants should invest more biomass on leaf growth rather than leaf structures per unit area to achieve a higher growth rate.Overall, variation in leaf functional traits among different groups may play an adaptive role in the successful survival of plants under diverse environments because leaf functional traits can lead to pronounced effects on leaf function,especially the acquisition and use of light. Plant species with different growth and leaf traits balance resource acquisition and leaf construction to minimize trade-offs and achieve fitness advantages in their natural habitat.
基金the National Natural Science Foundation of China under Grant Nos. 61028009, U0835002,and 61175065Natural Science Foundation of Anhui Province of China under Grant No. 1108085J16the Open Research Fundof State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of China under Grant No. 10R04
文摘Differential Evolution （DE） has been well accepted ever, it usually involves a large number of fitness evaluations to as an effective evolutionary optimization technique. Howobtain a satisfactory solution. This disadvantage severely restricts its application to computationally expensive problems, for which a single fitness evaluation can be highly timeconsuming. In the past decade, a lot of investigations have been conducted to incorporate a surrogate model into an evolutionary algorithm （EA） to alleviate its computational burden in this scenario. However, only limited work was devoted to DE. More importantly, although various types of surrogate models, such as regression, ranking, and classification models, have been investigated separately, none of them consistently outperforms others. In this paper, we propose to construct a surrogate model by combining both regression and classification techniques. It is shown that due to the specific selection strategy of DE, a synergy can be established between these two types of models, and leads to a surrogate model that is more appropriate for DE. A novel surrogate model-assisted DE, named Classification- and Regression-Assisted DE （CRADE） is proposed on this basis. Experimental studies are carried out on a set of 16 benchmark functions, and CRADE has shown significant superiority over DE-assisted with only regression or classification models. Further comparison to three state-of-the-art DE variants, i.e., DE with global and local neighborhoods （DECL）, JADE, and composite DE （CODE）, also demonstrates the superiority of CRADE.
文摘The paper deals with the application of multiwall carbon nanotubes(CNTs) to the adsorption of dyes from wastewater. Textile dyes are dangerous and diffused pollutant in wastewater, and the paper results confirmed the good adsorption ability of CNTs, with respect to classic active carbon, even for different dye types. The effect of surface treatments of CNTs was primarily investigated, revealing that neither the presence of residual catalyst nor common surface treatment(oxidation) affects the CNT's performances. Therefore less expensive nonpurified CNTs were assessed as good and economically convenient alternative for the process. In order to gain in generality in adsorption kinetic modelling, the parameters of the "best fitting" pseudo-second order model have been correlated to the main process variables(the dye initial concentration and the specific mass of CNTs.) setting-up a predictive kinetic model useful design new application of these materials in currently operating industrial operations for adsorption. In addition, isothermal data were used to screen all the relevant adsorption isotherms models and the Temkin model was confirmed as the more effective to accurately fit equilibrium data for any of the considered different dye types.
基金supported by National Key Research and Development Program of China (No. 2019YFB2102102)the Outstanding Youth Science Foundation (No. 61822602)+3 种基金National Natural Science Foundations of China (Nos. 62176094, 61772207 and 61873097)the Key-Area Research and Development of Guangdong Province (No. 2020B010166002)Guangdong Natural Science Foundation Research Team (No. 2018B030312003)National Research Foundation of Korea (No. NRF-2021H1D3A2A01082705)。
文摘Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation(EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applicatio
文摘Mandy:Wow!Your mobile phone is so cool!Dan:Thanks,it's new.My parents bought it for me.Mandy:My parents would never buy me an expensive phone.You're so lucky.Dan:Maybe I am,maybe I'm not.Mandy:Why do you say that?Dan:Because of my phone,I broke my arm.
基金co-supported by the National Natural Science Foundation of China(Nos.52272360,52232014,52005288,52201327)Beijing Natural Science Foundation,China(No.3222019)+1 种基金Beijing Institute of Technology Research Fund Program for Young Scholars,China(No.XSQD-202101006)BIT Research and Innovation Promoting Project(No.2022YCXZ017).
文摘To address the challenges of high-dimensional constrained optimization problems with expensive simulation models,a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling(SADE-MLS)is proposed.In SADE-MLS,differential evolution operators are executed to generate numerous high-dimensional candidate points.To alleviate the curse of dimensionality,a Manifold Learning-based Sampling(MLS)mechanism is developed to explore the high-dimensional design space effectively.In MLS,the intrinsic dimensionality of the candidate points is determined by a maximum likelihood estimator.Then,the candidate points are mapped into a low-dimensional space using the dimensionality reduction technique,which can avoid significant information loss during dimensionality reduction.Thus,Kriging surrogates are constructed in the low-dimensional space to predict the responses of the mapped candidate points.The candidate points with high constrained expected improvement values are selected for global exploration.Moreover,the local search process assisted by radial basis function and differential evolution is performed to exploit the design space efficiently.Several numerical benchmarks are tested to compare SADE-MLS with other algorithms.Finally,SADE-MLS is successfully applied to a solid rocket motor multidisciplinary optimization problem and a re-entry vehicle aerodynamic optimization problem,with the total impulse and lift to drag ratio being increased by 32.7%and 35.5%,respec-tively.The optimization results demonstrate the practicality and effectiveness of the proposed method in real engineering practices.
文摘Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.
基金This work was supported by the National Natural Science Foundation of China(Nos.52005288 and 51675047)the Aeronautical Science Foundation of China(No.2019ZC072003).
文摘The design of complex aerospace systems is a multidisciplinary design optimization(MDO)problem involving the interaction of multiple disciplines.However,because of the necessity of evaluating expensive black-box simulations,the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice.To resolve this,metamodel-based design optimization techniques have been applied to MDO.With these methods,system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency.This paper presents an overall survey of metamodel-based MDO for aerospace systems.From the perspective of aerospace system design,this paper introduces the fundamental methodology and technology of metamodel-based MDO,including aerospace system MDO problem formulation,metamodeling techniques,state-of-the-art metamodel-based multidisciplinary optimization strategies,and expensive black-box constraint-handling mechanisms.Moreover,various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering.The conclusions derived from this work are summarized in the final section of the paper.The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.
文摘Medical education is associated with significant costs[1].These costs have led to a growing interest in how to deliver high quality or high quantity education on a limited budget.This in turn has led to an interest in how best to measure quantity,quality and cost and how to track these variables over time.The ultimate aim of the interest in cost and value in medical education is to
基金Project supported by the Plan for the growth of young teachers,the National Natural Science Foundation of China(No.51505138)the National 973 Program of China(No.2010CB328005)+1 种基金Outstanding Youth Foundation of NSFC(No.50625519)Program for Changjiang Scholars
文摘In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive points,with two different search strategies respectively applied inside and outside the promising region.Besides,the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems.Tested upon a serial of benchmark math functions,the HMDSD method shows great efficiency and search accuracy.On top of that,a practical lightweight design demonstrates its superior performance.
文摘In recent years there has been increasing demand for natural non-nutritive high intensity sweeteners with low-calorie value as an alternative to sucrose. Extracts of the leaves of Stevia rebaudiana (Bertoni), have been known for their sweet taste. Steviosides and rebaudioside-A are the two major diterpenoid glucosides components present in the leaf extracts of the stevia, these glycosides are 300 times sweeter than sugar and also exhibits wide therapeutic activity. The conventional methods of isolation of steviosides involve long extraction and purification procedures;therefore optimization of product yields is a challenging problem. The present study, establishes a new improvised process of extraction of steviosides from the stevia leaves in which the dry treated leaves were grounded, defatted, and extracted through pressurized hot water extractor (PHWE), followed by purification and concentration of the sweet glycosides through ultra (UF) and nano (NF) membrane filtration in obtaining high (98.2%) purity steviosides. This process established “green” method for isolation of high quality steviol glycosides, with improved final yield is 10.1% from 11% of crude leaf extract and observed the improved organoleptic and biological activity (antioxidant). Thus the method confirms a simple, inexpensive and eco-friendly process in obtaining pure steviosides.
文摘Saffron is the most precious and expensive agricultural product. A dehydration treatment is necessary to convert Crocus sativus L. stigmas into saffron spice. To the best of our knowledge, no information on mass transfer parameters of saffron stigmas is available in the literature. This study aimed at investigating the moisture transfer parameters and quality attributes of saffron stigmas under infrared treatment at different temperatures（60,70, …, 110 ℃）. It was observed that the dehydration process of the samples occurred in a short accelerating rate period at the start followed by a falling rate period. The effective moisture diffusivity and convective mass transfer coefficient were determined by using the Dincer and Dost model. The diffusivity values varied from1.1103 × 10^-10m^2·s^-1to 4.1397 × 10^-10m^2·s^-1 and mass transfer coefficient varied in the range of 2.6433 × 10^-7–8.7203 × 10^-7m·s^-1. The activation energy was obtained to be 27.86 kJ·mol^-1. The quality assessment results showed that the total crocin content increased, when the temperature increased up to90 ℃ but, in higher temperatures, the amount of crocin decreased slightly. The total safranal content of the samples decreased slightly when drying temperature increased from 60 ℃ to 70 ℃ and then continuously increased up to 110 ℃. Also, the amount of picrocrocin increased from 83.1 to 93.3 as the drying temperature increased from 60 ℃ to 100 ℃.
基金supported in part by the National Natural Science Foundation of China(72171172,62088101)in part by the Shanghai Science and Technology Major Special Project of Shanghai Development and Reform Commission(2021SHZDZX0100)+2 种基金in part by the Shanghai Commission of Science and Technology(19511132100,19511132101)in part by the China Scholarship Councilin part by the Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia(FP-146-43)。
文摘This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction tool.The search operation conducted in this low space facilitates the population with fast convergence towards the optima.To strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process.Also,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed.The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200.As indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer.Compared with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.
文摘INTRODUCTION Campus heating and cooling systems present a particular challenge to the incorporation of advances in technology to improve efficiency.An older system is generally more expensive to operate than a newer one would be,but extensive upgrades are often difficult.Lengthy shutdowns affect multiple buildings,and new central heating and cooling equipment is very expensive.Buildings added over the years incorporate various types of systems,affecting central plant operation.Buried distribution piping is costly and can be disruptive to modify.
基金supported by the National Natural Science Foundation of China(No.61505142)the Tianjin Natural Science Foundation(No.16JCQNJC02100)
文摘The prediction method of dynamic wavelength is proposed for temperature tuning process. The temperature of the thermistor integrated in laser diode(LD) module is recorded to predict the LD chip temperature. Then according to the injection current and priori tuning characteristics of the LDs, the emission wavelength is estimated in real time. The method is validated by using a 1.58 μm distributed feedback(DFB) LD. The absorption spectra of mixture gas of CO_2 and CO are measured by means of the thermal tuning gas sensing system. The center wavelength of each absorption line is compared with the data in HITRAN2012 database. The results show that the deviations are less than 5 pm. This method fully meets the needs of spectroscopic measurement, and can be applied to spectroscopy, optical communications and other fields.
