In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become i...In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.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
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
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
For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs)....For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.展开更多
If you’ve been shelling out for designer clothing in the hopes that it will be more durable than cheaper options, you might want to reconsider it. A new study from The University of Leeds suggests that low-cost cloth...If you’ve been shelling out for designer clothing in the hopes that it will be more durable than cheaper options, you might want to reconsider it. A new study from The University of Leeds suggests that low-cost clothing might actually outlast pricier pieces.展开更多
When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by usin...When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency.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 applications more efficiently.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
China's real estate market presents a dilemma. Demand for housing continues to climb, but so does the number of vacant houses as developers hold fast to prices that few urban residents can afford
Some optimization problems in scientific research,such as the robustness optimization for the Internet of Things and the neural architecture search,are large-scale in decision space and expensive for objective evaluat...Some optimization problems in scientific research,such as the robustness optimization for the Internet of Things and the neural architecture search,are large-scale in decision space and expensive for objective evaluation.In order to get a good solution in a limited budget for the large-scale expensive optimization,a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems.A surrogate model is then trained for each sub-problem using different strategies to select training data adaptively.After that,a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem optimization.Furthermore,an escape mechanism is proposed to keep the diversity of the population.The performance of the method is evaluated on CEC’2013 benchmark functions.Experimental results show that the algorithm has better performance in solving expensive large-scale optimization problems.展开更多
Singapore might be where the film Crazy Rich Asians is set,but in terms of the most expensive city for luxury living,Shanghai is at the top of the list among all metropolises in the continent.
根据一家人力资源顾问公司(Mercer Human Resource Consulting)公布的全球生活指数调查的结果,香港的生活指数已经取代日本东京而成为全球生活指数最高的城市。第2位是莫斯科,东京则下跌为第3位。根据调查,香港生活指数上升,主要是个人...根据一家人力资源顾问公司(Mercer Human Resource Consulting)公布的全球生活指数调查的结果,香港的生活指数已经取代日本东京而成为全球生活指数最高的城市。第2位是莫斯科,东京则下跌为第3位。根据调查,香港生活指数上升,主要是个人护理、家居用品和交通项目费用高昂所致。调查同时研究了144个城市,比较其住房、食品、衣服、交通和娱乐等超过200项消费项目。在全球15个生活指数最高的城市当中,有11个位于亚洲,北京与上海分别排行第4位和第5位。调查发现,新西兰和澳大利亚的城市依然是生活费用便宜但生活质量最高的地方,大多数澳大利亚和新西兰城市的生活指数都在全球生活费用最高昂城市的一半以下。至于生活指数最低的城市则是南非的约翰内斯堡。布宜诺斯艾利斯(阿根廷首都)的生活指数则因国内经济不景气,而有一个dramatic fall。本文文字表达不乏可记学之处,如Moscow muscles in at secondplace in the survey一句中的muscles in就值得咀嚼一番。它是美国俚语,意思是:发挥臂力;靠力气前进,硬性挤入。另如:muscle through a crowd/用力挤过人群;muscle in on a展开更多
Cardiovascular diseases(CVD)and their risk factors are exerting an increasingly significant impact on public health,and the incidence rate of CVD continues to rise.This article provides an interpretation of essentials...Cardiovascular diseases(CVD)and their risk factors are exerting an increasingly significant impact on public health,and the incidence rate of CVD continues to rise.This article provides an interpretation of essentials from the newly published Annual Report on Cardiovascular Health and Diseases in China(2024),aiming to offer scientific evidence for CVD prevention,treatment,and the formulation of relevant policies.展开更多
IN the Shanghai Jewish Refugees Museum,a beautiful handbag is waiting for its owner in a display cabinet.The handbag belongs to a Jewish couple.When they sought refuge in Shanghai during World War II,they pawned the h...IN the Shanghai Jewish Refugees Museum,a beautiful handbag is waiting for its owner in a display cabinet.The handbag belongs to a Jewish couple.When they sought refuge in Shanghai during World War II,they pawned the handbag to Jin Wenzhen’s grandfather in exchange for their child’s medical expenses.He lent the couple the cash equivalent of one month’s revenue from his rice shop,but then never saw them again.展开更多
英汉语均广泛使用比较级,但英语用比较级更广泛些,花样也多些。因此,相对说来,英译汉困难多些。其实汉译英也不易,只是一般采用平铺直叙的译法,不常采用英语丰富多采的表达方式来翻译。例如下面提到的“绝不坏”,一般就译为“not bat at...英汉语均广泛使用比较级,但英语用比较级更广泛些,花样也多些。因此,相对说来,英译汉困难多些。其实汉译英也不易,只是一般采用平铺直叙的译法,不常采用英语丰富多采的表达方式来翻译。例如下面提到的“绝不坏”,一般就译为“not bat at all”,“not absolutely bad”或“quite good”,很少译为“no less good”。又如“这种布由于很漂亮,也就不算贵了”,一般直译为“This展开更多
有报道称,中国财富性别差距日益扩大,女性不仅被＂剩下＂,而且被＂无视＂。
Shang Wen,a single 32-year-old,a three-year-old’s mother,is a rarity1in China,mainly because her parents helped her purchase her own house in 20...有报道称,中国财富性别差距日益扩大,女性不仅被＂剩下＂,而且被＂无视＂。
Shang Wen,a single 32-year-old,a three-year-old’s mother,is a rarity1in China,mainly because her parents helped her purchase her own house in 2004.＂Property was not too expensive then,and my parents had no idea that the market would take off2,＂says Shang.Little did she know that this investment could end up paying off years later,展开更多
基金Sponsored by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2022L294)Taiyuan University of Science and Technology Scientific Research Initial Funding(Grant Nos.W2022018,W20242012)Foundamental Research Program of Shanxi Province(Grant No.202403021212170).
文摘In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.
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.
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.
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For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.
文摘If you’ve been shelling out for designer clothing in the hopes that it will be more durable than cheaper options, you might want to reconsider it. A new study from The University of Leeds suggests that low-cost clothing might actually outlast pricier pieces.
基金supported in part by the National Natural Science Foundation of China(51775385)the Natural Science Foundation of Shanghai(23ZR1466000)+2 种基金the Shanghai Industrial Collaborative Science and Technology Innovation Project(2021-cyxt2-kj10)the Innovation Program of Shanghai Municipal Education Commission(202101070007E00098)Fundo para o Desenvolvimento das Ciencias e da Tecnologia(FDCT)(0147/2024/AFJ).
文摘When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency.
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 applications more efficiently.
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China's real estate market presents a dilemma. Demand for housing continues to climb, but so does the number of vacant houses as developers hold fast to prices that few urban residents can afford
基金This work was supported in part by the National Natural Science Foundation of China(No.61876123)Shanxi Key Research and Development Program(No.202102020101002)Natural Science Foundation of Shanxi Province(Nos.201901D111264 and 201901D111262).
文摘Some optimization problems in scientific research,such as the robustness optimization for the Internet of Things and the neural architecture search,are large-scale in decision space and expensive for objective evaluation.In order to get a good solution in a limited budget for the large-scale expensive optimization,a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems.A surrogate model is then trained for each sub-problem using different strategies to select training data adaptively.After that,a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem optimization.Furthermore,an escape mechanism is proposed to keep the diversity of the population.The performance of the method is evaluated on CEC’2013 benchmark functions.Experimental results show that the algorithm has better performance in solving expensive large-scale optimization problems.
文摘Singapore might be where the film Crazy Rich Asians is set,but in terms of the most expensive city for luxury living,Shanghai is at the top of the list among all metropolises in the continent.
文摘根据一家人力资源顾问公司(Mercer Human Resource Consulting)公布的全球生活指数调查的结果,香港的生活指数已经取代日本东京而成为全球生活指数最高的城市。第2位是莫斯科,东京则下跌为第3位。根据调查,香港生活指数上升,主要是个人护理、家居用品和交通项目费用高昂所致。调查同时研究了144个城市,比较其住房、食品、衣服、交通和娱乐等超过200项消费项目。在全球15个生活指数最高的城市当中,有11个位于亚洲,北京与上海分别排行第4位和第5位。调查发现,新西兰和澳大利亚的城市依然是生活费用便宜但生活质量最高的地方,大多数澳大利亚和新西兰城市的生活指数都在全球生活费用最高昂城市的一半以下。至于生活指数最低的城市则是南非的约翰内斯堡。布宜诺斯艾利斯(阿根廷首都)的生活指数则因国内经济不景气,而有一个dramatic fall。本文文字表达不乏可记学之处,如Moscow muscles in at secondplace in the survey一句中的muscles in就值得咀嚼一番。它是美国俚语,意思是:发挥臂力;靠力气前进,硬性挤入。另如:muscle through a crowd/用力挤过人群;muscle in on a
文摘Cardiovascular diseases(CVD)and their risk factors are exerting an increasingly significant impact on public health,and the incidence rate of CVD continues to rise.This article provides an interpretation of essentials from the newly published Annual Report on Cardiovascular Health and Diseases in China(2024),aiming to offer scientific evidence for CVD prevention,treatment,and the formulation of relevant policies.
文摘IN the Shanghai Jewish Refugees Museum,a beautiful handbag is waiting for its owner in a display cabinet.The handbag belongs to a Jewish couple.When they sought refuge in Shanghai during World War II,they pawned the handbag to Jin Wenzhen’s grandfather in exchange for their child’s medical expenses.He lent the couple the cash equivalent of one month’s revenue from his rice shop,but then never saw them again.
文摘英汉语均广泛使用比较级,但英语用比较级更广泛些,花样也多些。因此,相对说来,英译汉困难多些。其实汉译英也不易,只是一般采用平铺直叙的译法,不常采用英语丰富多采的表达方式来翻译。例如下面提到的“绝不坏”,一般就译为“not bat at all”,“not absolutely bad”或“quite good”,很少译为“no less good”。又如“这种布由于很漂亮,也就不算贵了”,一般直译为“This
文摘有报道称,中国财富性别差距日益扩大,女性不仅被＂剩下＂,而且被＂无视＂。
Shang Wen,a single 32-year-old,a three-year-old’s mother,is a rarity1in China,mainly because her parents helped her purchase her own house in 2004.＂Property was not too expensive then,and my parents had no idea that the market would take off2,＂says Shang.Little did she know that this investment could end up paying off years later,
