Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support ...Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.展开更多
Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss...Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified.展开更多
This paper formulates a novel integrated measure for energy market efficiency,by investigating different aspects of the market performance.Different from most existing models focusing on one certain aspect,the novel m...This paper formulates a novel integrated measure for energy market efficiency,by investigating different aspects of the market performance.Different from most existing models focusing on one certain aspect,the novel measure especially takes into consideration the self-similarity(or system memo ability or long-term persistence)via fractality,the attractor properties in phase-space via chaos,and disorder state of data dynamics via entropy.In the proposed method,the most popular data analysis techniques of multi-fractal detrended fluctuation analysis,correlation dimension,and sample entropy are respectively conducted on the market returns to capture the corresponding features,and the entropy weight method is then used to generate the final integrated index.For illustration and verification,the proposed measure is applied to three typical energy markets analyses.The empirical results find that natural gas market and crude oil market are much more efficient than carbon market.展开更多
基金supported by the National Science Fund for Distinguished Young Scholars under Grant No.71025005the National Natural Science Foundation of China under Grant Nos.91224001 and 71301006+1 种基金National Program for Support of Top-Notch Young Professionalsthe Fundamental Research Funds for the Central Universities in BUCT
文摘Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.
基金supported by the Fundamental Research Funds for the Central Scientific Research Institutes (Grant No. 20200306)。
文摘Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified.
基金supported by the Major Program of the National Fund of Philosophy and Social Science of China under Grant No.18ZDA106
文摘This paper formulates a novel integrated measure for energy market efficiency,by investigating different aspects of the market performance.Different from most existing models focusing on one certain aspect,the novel measure especially takes into consideration the self-similarity(or system memo ability or long-term persistence)via fractality,the attractor properties in phase-space via chaos,and disorder state of data dynamics via entropy.In the proposed method,the most popular data analysis techniques of multi-fractal detrended fluctuation analysis,correlation dimension,and sample entropy are respectively conducted on the market returns to capture the corresponding features,and the entropy weight method is then used to generate the final integrated index.For illustration and verification,the proposed measure is applied to three typical energy markets analyses.The empirical results find that natural gas market and crude oil market are much more efficient than carbon market.
