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International Conference on Natural Computation (ICNC)

作者： Feng-gang Li Wei-feng Gong Ying Li Yu Liang Xiao-lu Wang Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China

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Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

Photovoltaic Power Forecasting Based on SVM Optimized by Imp...

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作者： Lin Liu Yanyun Lu Shuqin Li School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power.

关键词： Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

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Novel correlation coefficients for hesitant fuzzy sets and their applications to supplier selection and medical diagnosis

Novel correlation coefficients for hesitant fuzzy sets and t...

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作者： Yang, Jinxin Tang, Xiaoan Yang, Shanlin School of Management Hefei University of Technology Box 270 Hefei Anhui230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Anhui China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Hesitant fuzzy set theory provides an effective technique for researchers and engineers to cope with vagueness and uncertainty. In recent years, to explore the correlation between hesitant fuzzy sets, traditional correlation measure in statistics has been constantly studied in hesitant fuzzy environments. In this study, extant studies of correlation measures in hesitant fuzzy contexts are recalled and analyzed. In view of the forgoing analysis, we find out that the extant correlation coefficients have some limitations. Moreover, a few correlation coefficients are not in line with the traditional definition of correlation coefficients. In order to address the flaws of the existing proposals, a novel hesitant fuzzy correlation coefficient is proposed in this study. The new proposal of this study can not only overcome the flaws of the old hesitant fuzzy correlation coefficients, but it also shows several desirable characteristics. The weighted form of the newly defined correlation coefficient and its features are also investigated. Finally, three numerical examples concerning supplier selection and medical diagnosis are examined using the developed correlation coefficients to demonstrate their applicability. Comparison analyses with existing proposals highlight the efficiency of our proposals. © 2018 - IOS Press and the authors. All rights reserved.

关键词： decision making

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The forecasting of weekly tourists visiting a scenic area in off-season by using goodness of fit weight

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International Journal of Smart Home 2016年第3期10卷 21-30页

作者： Ma, Yin-Chao Liang, Chang-Yong Lu, Cai-Hona School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

Due to the impact of external objective factors, the weekly number of tourist distribution is imbalance and shows strong nonlinear characteristics, especially more obvious in the off-season. Aiming at this problem, this paper built two different forecasting models by SVR and BP neural network in order to guess about the weekly tourist numbers and then, put forward the Goodness of Fit Weight (GFW) and used it to make three SVR-BP combination models (LCM, GEOM-WTD, and HARM). In order to support the results empirically, Data of the Huangshan Scenic Area has been used and the dynamic virtual variable was adopted to deal with the objective factors that influence the weekly visitor numbers. Finally, we compare the results with the two combination models that based on the VARW and MSEW, it shows that the GFW combination model is helpful for the combination forecasting and can be used as one of selections just like other weights, and the use of dynamic virtual variables can further improve the forecasting precision. © 2016 SERSC.

关键词： Forecasting

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An improved discrete optimization algorithm based on artificial fish swarm and its application for attribute reduction

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Journal of Information and Computational Science 2015年第6期12卷 2143-2154页

作者： Ni, Zhiwei Zhu, Xuhui Ni, Liping Cheng, Meiying Wang, Yiling School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei China

The discrete Artificial Fish Swarm Algorithm (AFSA) has some defectives, such as falling into local optimum value, converging slowly. In order to overcome these shortcomings, an improved discrete optimization algorithm based on artificial fish swarm (IDAFSA) is proposed by improving swarming and preying behavior and moving way for making it discrete, and introducing a strategy of overcoming the local optimum. In addition, the paper strengthens the theoretical basis of the algorithm by proving the global convergence. And then, this algorithm which was combined with fractal dimension is applied to attribute reduction problem. Finally, analyzing the parameters in detail and comparing with other literatures by testing six UCI datasets, and the experimental results show that the method has relatively high feasibility and effectiveness. Copyright © 2015 Binary Information Press.

关键词： Fractal dimension

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Binary ant colony algorithm with particle optimization feature for attribute reduction problem

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Journal of Information and Computational Science 2015年第8期12卷 3051-3061页

作者： Ni, Zhiwei Cheng, Meiying Wu, Zhangjun Xiao, Hongwang Zhu, Xuhui School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei China

According to the lack of initial pheromone which leads to the long searching time of the Binary Ant Colony algorithm (BACO), a novel method named Binary Ant Colony Algorithm with Particle optimization Feature (PBACO) is proposed in this paper. Starting from one-dimensional cellular automata model, the initial solution is produced by Binary Particle Swarm optimization (BPSO). Then mapping mechanism is introduced to convert the position of particle into pheromone, in this way, a path with distinct discrepancy level of pheromone is generated in the shortest time, and the total searching time of BACO is greatly reduced. At last, PBACO combined with fractal dimension is utilized to solve attribute reduction problem. Six datasets are used to conduct experiments. Experimental results show that our proposed method has comparatively high classification accuracy. Copyright © 2015 Binary Information Press.

关键词： Cellular automata

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The selection of project management software by FAHP and FMCDM in automobile R&D process

The selection of project management software by FAHP and FMC...

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International Conference on Networking and Digital Society, ICNDS

作者： Lei Fu Li Shi Ying Yang Bengong Yu Key Laboratory of Process Optimization & Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781424451623;9781424451616

The selection of project management (PM) software is a complex issue and has significant impacts to the efficiency of the automobile research & development (R&D) process. Given two alternatives based on critical path method (CPM) and critical chain project management (CCPM) respectively, decision-makers usually cause confusion. This paper analyzed the characteristic of automobile R&D project and developed an evaluation model based on the fuzzy analytic hierarchy process (FAHP) and fuzzy multiple criteria decision making (FMCDM) methods, which can help the managers in automobile industry to select more appropriate software in the course of product R&D. In particular, the FAHP method is used to obtain the weights of evaluation criteria, and the FMCDM method is used to determine the final rank of the software. The uncertainty and vagueness in evaluation procedure were presented as the fuzzy triangular numbers. The proposed method is applied to the case study of evaluating the performance of PM software based on CPM and CCPM respectively for a car manufacturer. The result of evaluation not only assisted managers in choosing suitable software but also outline the trend in development of CCPM software.

关键词： Project management Automobiles Research and development Research and development management Software development management decision making Computer industry Software performance Application software Job shop scheduling

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Novel distance and similarity measures for hesitant fuzzy sets and their applications to multiple attribute decision making

Novel distance and similarity measures for hesitant fuzzy se...

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作者： Tang, Xiaoan Peng, Zhanglin Ding, Haining Cheng, Manli Yang, Shanlin School of Management Hefei University of Technology Hefei Box 270 Hefei Anhui230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui China School of Management North Minzu University Ningxia Hui Autonomous Region China

Distance and similarity measures have recently been investigated in-depth within the context of hesitant fuzzy sets. By analyzing the existing studies concerning distance measures for hesitant fuzzy sets, we find that they have some limitations. To address the flaws, this study develops some novel distance measures for hesitant fuzzy sets, including the normalized Euclidean distance measure, the Hausdorff metric distance measure, the normalized generalized distance measure, and their corresponding weighted distance measures. The proposals of this study not only hold many ideal characteristics but also do not consider the lengths of hesitant fuzzy elements as well as the arrangement of their possible values. To deal with the situations where both of the universe of discourse and the weight of element are continuous, some continuous hesitant fuzzy distance measures are also investigated. Based on the relationship between distance measure and similarity measure, some novel similarity measures for hesitant fuzzy sets can be further deduced from the proposed distance measures. Finally, two numerical examples are given to demonstrate the applicability and validity of the proposed hesitant fuzzy distance measures. © 2018 - IOS Press and the authors. All rights reserved.

关键词： decision making

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Exploring the impact of COVID-19 on travel behavior based on EV charging big data

Exploring the impact of COVID-19 on travel behavior based on...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Dingding Hu Fangyi Li Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3 % compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events.

关键词： COVID-19 Reactive power Pandemics Atmospheric modeling Big Data Air quality Electric vehicle charging

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Compression of OLAP Cubes for Aggregate Queries Based on Copula Approach

Compression of OLAP Cubes for Aggregate Queries Based on Cop...

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International Conference on Business Intelligence and Financial Engineering, BIFE

作者： Yazhuo Gao Zhiwei Ni Liping Ni School of Management Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China

This paper introduces Copula approach, which has been widely used in statistical field, to the construction of OLAP cubes for the first time. Based on this approach, a novel scheme is proposed to compress data and answer any OLAP query without accessing raw data. The procedure of this scheme can be generally divided into three steps. Firstly, find the proper distribution functions to fit the marginal distribution of each attribute. Secondly, employ Copula approach to catch the intra relationship among attributes in a data set. Finally, use the probability density function of joint distribution to calculate the aggregation function. Empirical evidence shows that this Copula-based model can not only drastically reduce storage requirements but also save the query response time, while the accuracy is bounded under a given level.

关键词： Data models Aggregates Joints Mathematical model Distribution functions Equations Computational modeling

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