Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557755
H. Rahman, R. Sarker, D. Essam
The Permutation Flow Shop Scheduling Problem (PFSP) is a well-known combinatorial optimization problem. In this paper, a Genetic Algorithm (GA) based approach has been developed to solve PFSP, with the objective of minimizing the makespan for a set of jobs. Two new priority rules; such as Gap Filling (GF) technique and Job Shifting (JS), have been introduced to enhance the performance of the GA. The algorithm has been used to solve a set of standard benchmark problems and the results have been compared with state-of-the-art algorithms. The comparison demonstrates that the overall performance of the algorithm is quite satisfactory.
{"title":"A memetic algorithm for Permutation Flow Shop Problems","authors":"H. Rahman, R. Sarker, D. Essam","doi":"10.1109/CEC.2013.6557755","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557755","url":null,"abstract":"The Permutation Flow Shop Scheduling Problem (PFSP) is a well-known combinatorial optimization problem. In this paper, a Genetic Algorithm (GA) based approach has been developed to solve PFSP, with the objective of minimizing the makespan for a set of jobs. Two new priority rules; such as Gap Filling (GF) technique and Job Shifting (JS), have been introduced to enhance the performance of the GA. The algorithm has been used to solve a set of standard benchmark problems and the results have been compared with state-of-the-art algorithms. The comparison demonstrates that the overall performance of the algorithm is quite satisfactory.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114489539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557921
S. Hernández, G. Leguizamón, E. Mezura-Montes
Differential Evolution (DE) is an algorithm capable of solving complex optimization problems with and without constraints. As many of the population-based algorithms, DE is based on operators that evolve a numerical population through search operators. The differential mutation, one of the basic operators in the original version of the algorithm, provides population diversity through the evolution. In this paper we propose an extended version of a previously proposed hybrid DE including know two different mutation operators, which are not applied simultaneously. The first of them, our main contribution, is based on the exploitation of feasible areas to identify promising regions of search space. The second mutation operator is the classic differential mutation and it is applied towards produce a balance between exploration and exploitation as well as to improve the individuals obtained with our operator. An experimental study was performed by considering 18 functions presented for the “Single Objective Constrained Real-Parameter Optimization” of the special session of CEC2010. The results are compared with those obtained by Takahama and Sakai, winners that CEC2010 special session with εDEag algorithm. The obtained results show that our proposed approach is capable of finding solutions of higher quality for scalable problems of dimension 30 whereas the results for dimension 10 remains competitive with εDEag.
{"title":"A hybrid version of differential evolution with two differential mutation operators applied by stages","authors":"S. Hernández, G. Leguizamón, E. Mezura-Montes","doi":"10.1109/CEC.2013.6557921","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557921","url":null,"abstract":"Differential Evolution (DE) is an algorithm capable of solving complex optimization problems with and without constraints. As many of the population-based algorithms, DE is based on operators that evolve a numerical population through search operators. The differential mutation, one of the basic operators in the original version of the algorithm, provides population diversity through the evolution. In this paper we propose an extended version of a previously proposed hybrid DE including know two different mutation operators, which are not applied simultaneously. The first of them, our main contribution, is based on the exploitation of feasible areas to identify promising regions of search space. The second mutation operator is the classic differential mutation and it is applied towards produce a balance between exploration and exploitation as well as to improve the individuals obtained with our operator. An experimental study was performed by considering 18 functions presented for the “Single Objective Constrained Real-Parameter Optimization” of the special session of CEC2010. The results are compared with those obtained by Takahama and Sakai, winners that CEC2010 special session with εDEag algorithm. The obtained results show that our proposed approach is capable of finding solutions of higher quality for scalable problems of dimension 30 whereas the results for dimension 10 remains competitive with εDEag.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557605
Aihong Ren, Yuping Wang
In the real world, many decision making problems often need to be modeled as a class of bilevel programming problems where fuzzy random coefficients are contained in both objective functions and constraint functions. To deal with these problems, an interval programming approach based on the α-level set is proposed to determine the optimal value range containing the best and worst optimal values so as to provide more information for decision makers. Furthermore, by incorporating expectation optimization model into probabilistic chance constraints, the best and worst optimal problems are transformed into deterministic ones. In addition, an estimation of distribution algorithm is designed to derive the best and worst Stackelberg solutions. Finally, a numerical example is given to show the application of the proposed models and algorithm.
{"title":"An interval programming approach for bilevel linear programming problem with fuzzy random coefficients","authors":"Aihong Ren, Yuping Wang","doi":"10.1109/CEC.2013.6557605","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557605","url":null,"abstract":"In the real world, many decision making problems often need to be modeled as a class of bilevel programming problems where fuzzy random coefficients are contained in both objective functions and constraint functions. To deal with these problems, an interval programming approach based on the α-level set is proposed to determine the optimal value range containing the best and worst optimal values so as to provide more information for decision makers. Furthermore, by incorporating expectation optimization model into probabilistic chance constraints, the best and worst optimal problems are transformed into deterministic ones. In addition, an estimation of distribution algorithm is designed to derive the best and worst Stackelberg solutions. Finally, a numerical example is given to show the application of the proposed models and algorithm.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129815988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557960
Christian Domínguez-Medina, G. Rudolph, O. Schütze, H. Trautmann
Here we address the problem of computing finite size Hausdorff approximations of the Pareto front of four-objective optimization problems by means of evolutionary computing. Since many applications desire an approximation evenly spread along the Pareto front and approximations that are good in the Hausdorff sense are typically evenly spread along the Pareto front we consider three different evolutionary multi-objective algorithms tailored to that purpose, where two of them are based on the Part and Selection Algorithm (PSA). Finally, we present some numerical results indicating the strength of the novel methods.
{"title":"Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique","authors":"Christian Domínguez-Medina, G. Rudolph, O. Schütze, H. Trautmann","doi":"10.1109/CEC.2013.6557960","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557960","url":null,"abstract":"Here we address the problem of computing finite size Hausdorff approximations of the Pareto front of four-objective optimization problems by means of evolutionary computing. Since many applications desire an approximation evenly spread along the Pareto front and approximations that are good in the Hausdorff sense are typically evenly spread along the Pareto front we consider three different evolutionary multi-objective algorithms tailored to that purpose, where two of them are based on the Part and Selection Algorithm (PSA). Finally, we present some numerical results indicating the strength of the novel methods.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128774954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557555
Ryoji Tanabe, A. Fukunaga
Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.
{"title":"Success-history based parameter adaptation for Differential Evolution","authors":"Ryoji Tanabe, A. Fukunaga","doi":"10.1109/CEC.2013.6557555","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557555","url":null,"abstract":"Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129088858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557553
Johanna Aalto, J. Lampinen
A new adaptive Differential Evolution algorithm called EWMA-DE is proposed. In original Differential Evolution algorithm three different control parameter values must be pre-specified by the user a priori; Population size, crossover constant and mutation scale factor. Choosing good parameters can be very difficult for the user, especially for the practitioners. In the proposed algorithm the mutation scale factor is adapted using a novel exponential moving average based mechanism, while the other control parameters are kept fixed as in standard Differential Evolution. The algorithm was initially evaluated by using the set of 25 benchmark functions provided by CEC2005 special session on real-parameter optimization and compared with the results of standard DE/rand/1/bin version. Results turned out to be rather promising; EWMA-DE outperformed the original Differential Evolution in majority of tested cases, which is demonstrating the potential of the proposed adaptation approach.
{"title":"A mutation adaptation mechanism for Differential Evolution algorithm","authors":"Johanna Aalto, J. Lampinen","doi":"10.1109/CEC.2013.6557553","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557553","url":null,"abstract":"A new adaptive Differential Evolution algorithm called EWMA-DE is proposed. In original Differential Evolution algorithm three different control parameter values must be pre-specified by the user a priori; Population size, crossover constant and mutation scale factor. Choosing good parameters can be very difficult for the user, especially for the practitioners. In the proposed algorithm the mutation scale factor is adapted using a novel exponential moving average based mechanism, while the other control parameters are kept fixed as in standard Differential Evolution. The algorithm was initially evaluated by using the set of 25 benchmark functions provided by CEC2005 special session on real-parameter optimization and compared with the results of standard DE/rand/1/bin version. Results turned out to be rather promising; EWMA-DE outperformed the original Differential Evolution in majority of tested cases, which is demonstrating the potential of the proposed adaptation approach.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129153224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557788
Z. Cong, B. Schutter, Robert Babuška
Ant Colony Optimization (ACO) has proved to be a powerful metaheuristic for combinatorial optimization problems. From a theoretical point of view, the convergence of the ACO algorithm is an important issue. In this paper, we analyze the convergence properties of a recently introduced ACO algorithm, called ACO with stench pheromone (ACO-SP), which can be used to solve dynamic traffic routing problems through finding the minimum cost routes in a traffic network. This new algorithm has two different types of pheromone: the regular pheromone that is used to attract artificial ants to the arc in the network with the lowest cost, and the stench pheromone that is used to push ants away when too many ants converge to that arc. As a first step of a convergence proof for ACO-SP, we consider a network with two arcs. We show that the process of pheromone update will transit among different modes, and finally stay in a stable mode, thus proving convergence for this given case.
{"title":"On the convergence of Ant Colony Optimization with stench pheromone","authors":"Z. Cong, B. Schutter, Robert Babuška","doi":"10.1109/CEC.2013.6557788","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557788","url":null,"abstract":"Ant Colony Optimization (ACO) has proved to be a powerful metaheuristic for combinatorial optimization problems. From a theoretical point of view, the convergence of the ACO algorithm is an important issue. In this paper, we analyze the convergence properties of a recently introduced ACO algorithm, called ACO with stench pheromone (ACO-SP), which can be used to solve dynamic traffic routing problems through finding the minimum cost routes in a traffic network. This new algorithm has two different types of pheromone: the regular pheromone that is used to attract artificial ants to the arc in the network with the lowest cost, and the stench pheromone that is used to push ants away when too many ants converge to that arc. As a first step of a convergence proof for ACO-SP, we consider a network with two arcs. We show that the process of pheromone update will transit among different modes, and finally stay in a stable mode, thus proving convergence for this given case.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129452971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557566
Carlos R. B. Azevedo, F. V. Zuben
An anticipatory stochastic multi-objective model based on S-Metric maximization is proposed. The environment is assumed to be noisy and time-varying. This raises the question of how to incorporate anticipation in metaheuristics such that the Pareto optimal solutions can reflect the uncertainty about the subsequent environments. A principled anticipatory learning method for tracking the dynamics of the objective vectors is then proposed so that the estimated S-Metric contributions of each solution can integrate the underlying stochastic uncertainty. The proposal is assessed for minimum holding, cardinality constrained portfolio selection, using real-world stock data. Preliminary results suggest that, by taking into account the underlying uncertainty in the predictive knowledge provided by a Kalman filter, we were able to reduce the sum of squared errors prediction of the portfolios ex-post return and risk estimation in out-of-sample investment environments.
{"title":"Anticipatory Stochastic Multi-Objective Optimization for uncertainty handling in portfolio selection","authors":"Carlos R. B. Azevedo, F. V. Zuben","doi":"10.1109/CEC.2013.6557566","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557566","url":null,"abstract":"An anticipatory stochastic multi-objective model based on S-Metric maximization is proposed. The environment is assumed to be noisy and time-varying. This raises the question of how to incorporate anticipation in metaheuristics such that the Pareto optimal solutions can reflect the uncertainty about the subsequent environments. A principled anticipatory learning method for tracking the dynamics of the objective vectors is then proposed so that the estimated S-Metric contributions of each solution can integrate the underlying stochastic uncertainty. The proposal is assessed for minimum holding, cardinality constrained portfolio selection, using real-world stock data. Preliminary results suggest that, by taking into account the underlying uncertainty in the predictive knowledge provided by a Kalman filter, we were able to reduce the sum of squared errors prediction of the portfolios ex-post return and risk estimation in out-of-sample investment environments.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557823
Carlos R. B. Azevedo, F. V. Zuben
This paper proposes a regularized hypervolume (SMetric) selection algorithm. The proposal is used for incorporating stability and diversification in financial portfolios obtained by solving a temporal sequence of multi-objective Mean Variance Problems (MVP) on real-world stock data, for short to longterm rebalancing periods. We also propose the usage of robust statistics for estimating the parameters of the assets returns distribution so that we are able to test two variants (with and without regularization) on dynamic environments under different levels of instability. The results suggest that the maximum attaining Sharpe Ratio portfolios obtained for the original MVP without regularization are unstable, yielding high turnover rates, whereas solving the robust MVP with regularization mitigated turnover, providing more stable solutions for unseen, dynamic environments. Finally, we report an apparent conflict between stability in the objective space and in the decision space.
{"title":"Regularized hypervolume selection for robust portfolio optimization in dynamic environments","authors":"Carlos R. B. Azevedo, F. V. Zuben","doi":"10.1109/CEC.2013.6557823","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557823","url":null,"abstract":"This paper proposes a regularized hypervolume (SMetric) selection algorithm. The proposal is used for incorporating stability and diversification in financial portfolios obtained by solving a temporal sequence of multi-objective Mean Variance Problems (MVP) on real-world stock data, for short to longterm rebalancing periods. We also propose the usage of robust statistics for estimating the parameters of the assets returns distribution so that we are able to test two variants (with and without regularization) on dynamic environments under different levels of instability. The results suggest that the maximum attaining Sharpe Ratio portfolios obtained for the original MVP without regularization are unstable, yielding high turnover rates, whereas solving the robust MVP with regularization mitigated turnover, providing more stable solutions for unseen, dynamic environments. Finally, we report an apparent conflict between stability in the objective space and in the decision space.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126895032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557757
Xuefeng Chen, Yi-feng Zeng, Y. Ong, Choon Sing Ho, Yanping Xiang
Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains.
{"title":"A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems","authors":"Xuefeng Chen, Yi-feng Zeng, Y. Ong, Choon Sing Ho, Yanping Xiang","doi":"10.1109/CEC.2013.6557757","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557757","url":null,"abstract":"Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123872610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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