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Empirical Study of Population-Based Dynamic Constrained Multimodal Optimization Algorithms

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Empirical Study of Population-Based Dynamic Constrained Mult...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Xin Lin Wenjian Luo Yingying Qiao Peilan Xu Tao Zhu Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei China School of Software Engineering University of South China Hengyang China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

There are many dynamic optimization problems in real-world applications. Although many variants of evolutionary algorithms and swarm intelligence have been proposed to solve such problems, little work has been conducted to address the dynamic constrained multimodal optimization problems (DCMMOPs). In DCMMOPs, there exist multiple optimal solutions corresponding to each environment that the algorithm is required to find. Sometimes, it is also necessary to identify the accepted local optima. Therefore, for each environment, the decision maker can select one from among multiple returned solutions according to his/her domain knowledge and/or preferences. The objective of this paper is to test the performance of various combinations of several population-based dynamic multimodal optimization algorithms and popular constraint handling techniques. First, the typical dynamic constrained optimization problems are slightly modified to be in the form of the dynamic constrained multimodal optimization problems. Second, four different population-based dynamic multimodal optimization algorithms, and five different constraint handling techniques, are pairwise tested in the experiments. Experimental results demonstrate that, among the candidates, DCMM-CSA-SR performs most successfully at all accuracy levels.

关键词： Heuristic algorithms Optimization Vehicle dynamics Sociology Statistics Evolutionary computation Clustering algorithms

Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users

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arXiv 2020年

作者： Lu, Huabing Xie, Xianzhong Shi, Zhaoyuan Lei, Hongjiang Yang, Helin Cai, Jun The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, which greatly improves the spectrum efficiency and effectively relieves the massive access problem of 5G and beyond networks. In this paper, we first study the outage performance of the greedy best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. In order to tackle the admission fairness problem of the BU-SGF scheme, we propose a fair SGF scheme by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), in which the GF user with the best channel relative to its own statistics will be admitted. Moreover, by employing the theories of order statistics and stochastic geometry, the outage performances of both BU-SGF and CS-SGF schemes are analyzed. Theoretical results show that both schemes can achieve full diversity orders only when the served users' data rate is capped, which severely limits the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive analytical expressions of the two schemes' outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrum efficiency

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

LVMapper: A large-variance clone detector using sequencing alignment approach

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arXiv 2019年

作者： Wu, Ming Wang, Pengcheng Yin, Kangqi Cheng, Haoyu Xu, Yun Roy, Chanchal K. School of Computer Science University of Science and Technology of China Hefei China Key Laboratory on High Performance Computing Anhui Province Hefei China Department of Computer Science University of Saskatchewan Saskatoon Canada

To detect large-variance code clones (i.e. clones with relatively more differences) in large-scale code repositories is difficult because most current tools can only detect almost identical or very similar clones. It will make promotion and changes to some software applications such as bug detection, code completion, software analysis, etc. Recently, CCAligner made an attempt to detect clones with relatively concentrated modifications called large-gap clones. Our contribution is to develop a novel and effective detection approach of large-variance clones to more general cases for not only the concentrated code modifications but also the scattered code modifications. A detector named LVMapper is proposed, borrowing and changing the approach of sequencing alignment in bioinformatics which can find two similar sequences with more differences. The ability of LVMapper was tested on both self-synthetic datasets and real cases, and the results show substantial improvement in detecting large-variance clones compared with other state-of-the-art tools including CCAligner. Furthermore, our new tool also presents good recall and precision for general Type-1, Type-2 and Type-3 clones on the widely used benchmarking dataset, BigCloneBench. Copyright © 2019, The Authors. All rights reserved.

关键词： Cloning

Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

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computers, Materials & Continua 2018年第6期55卷 495-507页

作者： Yang Du Zhaoxia Yin Xinpeng Zhang Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei230601P.R.China Department of Computer Science Purdue UniversityWest lafayette47906USA School of Communication and Information Engineering Shanghai UniversityShanghai 200072P.R.China

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram *** most in JPEG bitstream consists of a sequence of VLCs(variable length codes)and the appended *** VLC has a corresponding RLV(run/length value)to record the AC/DC *** achieve lossless data hiding with high payload,we shift the histogram of VLCs and modify the DHT segment to embed *** we sort the histogram of VLCs in descending order,the filesize expansion is *** paper’s key contribution includes:Lossless data hiding,less filesize expansion in identical pay-load and higher embedding efficiency.

关键词： Lossless data hiding histogram VLC JPEG.

Error-minimization approach to reduce protease bias 2

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Error-minimization approach to reduce protease bias

2nd International Conference on Biological Information and Biomedical Engineering, BIBE 2018

作者： Chen, Chong Zheng, Haoran School of Computer Science and Technology University of Science and Technology of China Hefei China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei China Department of Systems Biology University of Science and Technology of China Hefei China

ISBN: (纸本)9783800747276

The main goal in proteomics is to describe the proteome in a comprehensive and accurate way, and enzymolysis is a key step in the process of large-scale proteomics experiments. For the same protein samples, using different proteases may introduce a strong bias into protein analysis results. This situation will make proteomics experiments irreproducible and will have a serious impact on the experimental results. To reduce this protease bias, we present EMQ (Error-Minimization-based Quantification for Protein), an approach based on error minimization. In contrast to the existing method, our model includes information on shared peptides, which are ubiquitous in mass spectrometric datasets. The abundance of shared peptides will be assigned to related proteins accurately, thereby determining the protein abundance. Based on this feature, we used the proposed model with several datasets, and compared its performance with the other. Finally, our model is shown to have better performance than other approach (average Pearson correlation of ~0.83 obtained by EMQ, while ~0.63 by other model). © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Peptides

A mobile cloud-based ehealth scheme

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arXiv 2020年

作者： Liu, Yihe Abbasi, Aaqif Afzaal Aghaei, Atefeh Abbasi, Almas Mosavi, Amir Shamshirband, Shahab Al-qaness, Mohammed A.A. Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AI Neijiang Normal University Neijiang641100 China Department of Software Engineering Foundation University Islamabad44000 Pakistan Department of Communication Faculty of Social Sciences University of Tehran Tehran*** Iran Department of Computer Science International Islamic University Islamabad46000 Pakistan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Kalman Kando Faculty of Electrical Engineering Obuda University Budapest1034 Hungary School of the Built Environment Oxford Brookes University OxfordOX3 0BP United Kingdom Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam School of Computer Science Wuhan University Wuhan430072 China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： Signal analysis

Empirical topological investigation of practical supply chains based on complex networks

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Chinese Physics B 2017年第11期26卷 144-150页

作者：廖好沈婧吴兴桐陈博奎周明洋 National Engineering Laboratory for Big Data Computing Systems Guangdong Province Key Laboratory of Popular High Performance ComputersCollege of Computer Science and Software Engineering Shenzhen University Department of Computer Science School of Computing National University of Singapore

The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management.

关键词： China supply chain networks complex networks data science network science

A hybrid particle swarm optimization for high-dimensional dynamic optimization 11th

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A hybrid particle swarm optimization for high-dimensional dy...

11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Luo, Wenjian Yang, Bin Bu, Chenyang Lin, Xin Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319687582

High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases. © Springer International Publishing AG 2017.

关键词： Particle swarm optimization (PSO)