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A quantitative pairwise comparison-based constraint handling technique for constrained optimization

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arXiv 2023年

作者： Huang, Ting Zhang, Qiang Pedrycz, Witold Yang, Shanlin School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Box 270 Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

This study proposes a new constraint handling technique for assisting metaheuristic optimization algorithms to solve constrained optimization problems more effectively and efficiently. Given any two solutions of any constrained optimization problems, they are first mapped into a two-dimensional Cartesian coordinate system with their objective function value differences and constraint violation differences as the two axes. To the best of our knowledge, we are the first to deal with constraints by building such a Cartesian coordinate system. Then, the Cartesian coordinate system is divided into a series of grids by assigning ranks to different intervals of differences. In this way, a pairwise comparison criterion is derived with the use of the fused ranks, which achieves non-hierarchical comparison neither preferring objective function values nor constraint violations, resulting in more accurate evaluation compared with existing techniques. Moreover, an evaluation function that is equivalent to the pairwise comparison criterion is proposed, which further improves computational efficiency. The effectiveness and efficiency of the proposed constraint handling technique are verified on two well-known public datasets, that is, CEC 2006 and CEC 2017. The results demonstrate that metaheuristic optimization algorithms with using the proposed constraint handling technique can converge to a feasible optimal solution faster and more reliably. Experimental analysis on the parameters involved reveal guidance for their optimal *** Codes 90-08 (Primary) Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

Clustering ensembles algorithm based on fractal dimension

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 2012年第SUPPL. 1期42卷 364-367页

作者： Wu, Xiao-Xuan Ni, Zhi-Wei Ni, Li-Ping School of Management and Business Intelligence Institute Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei 230009 China

The fractal dimension based clustering ensembles algorithm was studied. It introduced clustering algorithm based on fractal dimension at first in order to create partitions for clustering ensembles, then using voting strategy to get ensembles result. Finally, an idea on distributed clustering ensembles under cloud computing environment was brief discussed. Fractal dimension based clustering ensembles algorithm can offer better solutions in terms of robustness, novelty and stability than the single clustering algorithm based on fractal dimension. Combining the approaches based on grid and fractal, the clustering algorithm called grid and fractal dimension based clustering algorithm (GFDC) was presented to create partitions for clustering ensembles instead of clustering algorithm based on fractal dimension. GFDC is able to capture arbitrary shapes and non-neighboring clustering and can be applied to the massive and high-dimension dataset.

关键词： Fractal dimension

Probabilistic Runoff Forecasting Considering Stepwise Decomposition Framework and External Feature Integration Structure

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SSRN

SSRN 2023年

作者： Cao, Chaojin He, Yaoyao Cai, Siyu School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Ministry of Education Hefei230009 China Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development Hefei University of Technology Hefei230009 China China Institute of Water Resources and Hydropower Research Beijing100038 China

As dramatic weather changes complexify the change of runoff and introduce significant uncertainties, probabilistic runoff forecasting becomes increasingly important for water resource managers because it can quantify the uncertainty in the prediction process. However, little research has been conducted on how to employ decomposition techniques rationally in probabilistic prediction while effectively considering external factors. To fill the gap, this paper proposes a novel framework for probabilistic runoff forecasting, which incorporates several key components. It first utilizes the stepwise decomposition (SD) technique to prevent the information leakage problem. Then an external feature integration structure is designed to enhance the learning ability of the neural network for various features and decomposition sequences. The boundary correction technique is implemented to mitigate the boundary distortion problem. This comprehensive framework is named SDIFC. Building upon this framework, we achieve a synergistic integration of three distinct methods: seasonal-trend decomposition procedures based on loess (STL), least absolute shrinkage and selection operator (LASSO), and quantile regression long short-term memory (QRLSTM). This method is referred to as STL-LASSO-QRLSTM. We employ two datasets from the Lutaizi and Wangjiaba stations in China to conduct case studies. Several hybrid models and comparison frameworks are developed to conduct adequate comparative experiments, which are evaluated using six metrics that measure deterministic and probabilistic performance. The findings show that the predictive power of the proposed model exceeds comparative methods based on the normal and SD frameworks. This research holds immense importance for developing probabilistic models based on deep learning for runoff forecasting. © 2023, The Authors. All rights reserved.

关键词： Forecasting

Solving Satellite Image Data Downlink Scheduling Problem with Family Attribute Via a Bi-Stage Differential Evolutionary Algorithm

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SSRN

SSRN 2023年

作者： Chang, Zhongxiang Zhou, Zhongbao Liu, Xiaolu School of Business Administration Hunan University Changsha410082 China Hunan Key Laboratory of Intelligent decision-Making Technology for Emergency Management Changsha410082 China School of System Engineering National University of Defense Technology Changsha410073 China

The asynchronous development between the observation capability and the transition capability results in that an original image data (OID) formed by one-time observation cannot be completely transmitted in one transmit chance between the EOS and GS (named as a visible time window, VTW). It needs to segment the OID to several segmented image data (SID) and then transmits them in several VTWs, which enriches the extension of satellite image data downlink scheduling problem (SIDSP). We define the novel SIDSP as satellite image data downlink scheduling problem with family attribute (SIDSPWFA), in which some big OID is segmented by a fast segmentation operator first, and all SID and other no-segmented OID is transmitted in the second step. Two optimization objectives, the image data transmission failure rate (FR) and the segmentation times (ST), are then designed to formalize SIDSPWFA as a bi-objective discrete optimization model. Furthermore, a bi-stage di erential evolutionary algorithm (DE+NSGA-II) is developed holding several bi-stage operators. Extensive simulation instances show the eciency of models, strategies, algorithms and operators is analysed in detail. © 2023, The Authors. All rights reserved.

关键词： Combinatorial optimization

An Empirical Study on Personality Characteristics of College Volunteers: Based on the Collected Data from Shanghai Expo

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Procedia - Social and Behavioral Sciences 2013年 83卷 969-974页

作者： Ya-di Gu Dong-xiao Gu Qing Wei Shen Pan Wen-en Chen Yi Ge Students’ Affairs Division at University of Shanghai for Science and Technology Shanghai200093 P.R. CHINA School of Management at Hefei University of Technology Hefei Anhui230009 P.R. CHINA School of Foreign Studies at Hefei University of Technology Hefei Anhui230009 P.R. CHINA Engineering Research Center of Intelligent Decision-Making and Information System Technology of Ministry of Education of China Hefei Anhui 230009 P.R. CHINA Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education of China Hefei Anhui 230009 P.R. CHINA Department of Electronic Information Technology at Tianjin Normal University Tianjin 300387 P.R. CHINA

Objective : This study is to investigate the personality characteristics of student volunteers, which would provide a reference to select and train qualified volunteers for the future. Methods : 233 student volunteers and 236 non-volunteers are selected for our study. Based on the data of 16PF personality exported from the database, statistical software SPSS15.0 is used to conduct data analysis. Result : Volunteers and non-volunteers have significant differences of personality: the 16 basic personality factors, the gregariousness, excitement, daring and independence scores are significantly different; in the other eight binary factors, emotional and serene in the alert type, introverted and extroverted type, timid and bold type factors have significantly different scores. Male and female volunteers have significant personality difference in some respects. Conclusion : College student volunteers have obvious personality traits of extroversion, optimism, cheerfulness, enthusiasm, self-confidence etc. In addition, male and female volunteers have gender differences in personality characteristics in some aspects.

关键词： College students Volunteers Personality traits Shanghai Expo Selection

Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

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IOP Conference Series: Earth and Environmental Science 2020年第5期474卷

作者： Lin Liu Yanyun Lu Shuqin Li School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 China

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.

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Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning

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arXiv 2023年

作者： Han, Zhi Liu, Baichen Lin, Shao-Bo Zhou, Ding-Xuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Center of Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics University of Sydney SydneyNSW2006 Australia

This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in enabling translation-equivalence, and pooling in its translation-invariance driven nature, we show that with similar number of free parameters, any deep fully connected networks (DFCNs) can be represented by DCNNs with zero-padding. This demonstrates that DCNNs with zero-padding is essentially better than DFCNs in feature extraction. Consequently, we derive universal consistency of DCNNs with zero-padding and show its translation-invariance in the learning process. All our theoretical results are verified by numerical experiments including both toy simulations and real-data running. © 2023, CC0.

关键词： Feature extraction

An adaptive bi-objective optimization algorithm for the satellite image data downlink scheduling problem considering request split

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arXiv 2022年

作者： Chang, Zhongxiang Punnen, Abraham P. Zhou, Zhongbao School of Business Administration Hunan University Changsha410082 China Department of Mathematics Simon Fraser University SurreyBCV3T 0A3 Canada Hunan Key Laboratory of Intelligent Decision-Making Technology for Emergency Management Changsha410082 China

The satellite image data downlink scheduling problem (SIDSP) is well studied in literature for traditional satellites. With recent developments in satellite technology, SIDSP for modern satellites became more complicated, adding new dimensions of complexities and additional opportunities for the effective use of the satellite. In this paper, we introduce the dynamic two-phase satellite image data downlink scheduling problem (D-SIDSP) which combines two interlinked operations of image data segmentation and image data downlink, in a dynamic way, and thereby offering additional modelling flexibility and renewed capabilities. D-SIDSP is formulated as a bi-objective problem of optimizing the image data transmission rate and the service-balance degree. Harnessing the power of an adaptive large neighborhood search algorithm (ALNS) with a nondominated sorting genetic algorithm II (NSGA-II), an adaptive biobjective memetic algorithm, ALNS+NSGA-II, is developed to solve D-SIDSP. Results of extensive computational experiments carried out using benchmark instances are also presented. Our experimental results disclose that the algorithm ALNS+NSGA-II is a viable alternative to solve D-SIDSP more efficiently and demonstrates superior outcomes based on various performance metrics. The paper also offers new benchmark instances for D-SIDSP that can be used in future research works on the topic. © 2022, CC BY-NC-ND.

关键词： Satellites

Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks

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arXiv 2023年

作者： Liu, Shuai Guo, Xiao Qi, Shun Wang, Huaning Chang, Xiangyu Department of Information Systems and Intelligent Business School of Management Xi’an Jiaotong University China Key Laboratory of Biomedical Information Engineering Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Xi’an Jiaotong University China Department of Psychiatry Xijing Hospital Air Force Medical University China Center for Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University China

Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance understanding of the disorder mechanisms and early intervention. However, due to the small sample size and the high dimension of available neuroimaging data, the performance of existing methods is often limited. Multi-site data could enhance the statistical power and sample size, while they are often subject to inter-site heterogeneity and data-sharing policies. In this paper, we propose a federated joint estimator, NOTEARS-PFL, for simultaneous learning of multiple Bayesian networks (BNs) with continuous optimization, to identify disease-induced alterations in MDD patients. We incorporate information shared between sites and site-specific information into the proposed federated learning framework to learn personalized BN structures by introducing the group fused lasso penalty. We develop the alternating direction method of multipliers, where in the local update step, the neuroimaging data is processed at each local site. Then the learned network structures are transmitted to the center for the global update. In particular, we derive a closed-form expression for the local update step and use the iterative proximal projection method to deal with the group fused lasso penalty in the global update step. We evaluate the performance of the proposed method on both synthetic and real-world multi-site rs-fMRI datasets. The results suggest that the proposed NOTEARS-PFL yields superior effectiveness and accuracy than the comparable methods. © 2023, CC BY.

关键词： Bayesian networks