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This document is a correction notice for an article titled "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting." The correction addresses a mistake in the legend for Tables 10 and 11, clarifying that the LS-SVR-CQGA model significantly outperforms the other models. The authors assure that this correction does not affect the scientific conclusions of the study. The original publication has been updated accordingly. [Extracted from the article]

The prediction of port throughput is very complicate, and its accuracy is affected by many socio-economic factors, particularly affected by their embedded distributed randomness of these factors and mixed noises produced in the processes of data collection, transformation, and calculation. Firstly, in view of the v-support vector regression hybridized with Gauss function (briefed as Gauss- vSVR model), to well solve the nonlinear and mixed noises, this paper uses this model to simulate the nonlinear evolving system of port throughput series. Then, to look for more suitable parameter combination of this model and take into account that GA still suffers from the problems of trapped into local optima and time-consuming, this study integrates the global chaotic perturbation algorithm by using Cat mapping function and local acceleration search algorithm by employing cloud theory, i.e., abbreviated as chaotic cloud genetic algorithm (CCGA), to well determine the parameter values for an Gauss- vSVR model. Additionally, based on the principal component analysis and correlation analysis method, an input vector decision method (namely IVD) is proposed to identify the final input variables for Gauss- vSVR model. Finally, hybridization of IVD and CCGA with Gauss- vSVR model, namely IG vSVR-CCGA, is proposed for port throughput forecasting. Subsequently, the port throughput data and its associate socio-economic factors of two largest Chinese ports, Shanghai Port and Tianjin Port, are employed as practical examples to test forecast performance. The numerical results indicate that the proposed hybrid forecasting model receives more satisfied forecasting performance than other classical prediction models; in the meanwhile, the CCGA algorithm also obtains higher optimal efficiency than other alternative algorithms. [ABSTRACT FROM AUTHOR]

Port throughput forecasting is a very complex nonlinear dynamic process, prediction accuracy is influenced by uncertainty of socio-economic factors, especially by the mixed noise (singular point) produced in the collection, transfer and calculation of statistical data; consequently, it is difficult to obtain a satisfactory port throughput forecasting result. Thus, establishing an effective port throughput forecasting scheme is still a significant research issue. Since the robust v -support vector regression model ( R SVR) has the ability to solve the nonlinear and mixed noise in the port throughput history data and its related socio-economic factors, this paper introduces the R SVR model to forecast port throughput. In order to search the more appropriate parameters combination for the R SVR model, considering the proposed simulated annealing particle swarm optimization (SAPSO) algorithm and the original PSO algorithm still have the drawbacks of immature convergence and is time consuming, this study presents chaotic simulated annealing particle swarm optimization(CSAPSO) algorithm to determine the parameter combination. Aiming to identify the final input vectors for R SVR model, the multivariable adaptive regression splines (MARS) is adopted to select the final input vectors from the candidate input variables. This study eventually proposes a port throughput forecasting scheme that hybridizes the R SVR, CSAPSO and MARS to obtain a more accurate forecasting result. Subsequently, this study compiles the port throughput data and the corresponding socio-economic indicators data of Shanghai as the illustrative example to evaluate the feasibility and performance of the proposed scheme. The experimental results indicate that the proposed port throughput forecasting scheme obtains better forecasting result than the six competing models in terms of forecasting error. [ABSTRACT FROM AUTHOR]

To improve urban passenger prediction accuracy with parametric projection pursuit regression, acceleration genetic algorithm was improved with cat map, Gaussian distribution and local searching. A new chaos accelerating genetic algorithm (NCAGA) was presented, used to optimize the best projection direction a of PPPR model. A hybrid algorithm of NCAGA-PPPR urban passenger forecasting model was proposed, in which the best projection direction was hybrid optimized inner by the NCAGA at the time of optimizing outer the number of ridge functions M. The simulation prediction was made with observed data, compared with BP neural network model, traditional PPR model and PPPR model optimized by acceleration genetic algorithm. The urban passenger forecasting accuracy is higher than the others, which the mean absolute relative error is less than 3.1%. The new hybrid algorithm can improve prediction accuracy of urban passenger and can be used efficaciously to forecast the urban passenger. [ABSTRACT FROM AUTHOR]