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First machine learning gravitational-wave search mock data challenge

Physical Review D 2023年第2期107卷 023021-023021页

作者： Marlin B. Schäfer Ondřej Zelenka Alexander H. Nitz He Wang Shichao Wu Zong-Kuan Guo Zhoujian Cao Zhixiang Ren Paraskevi Nousi Nikolaos Stergioulas Panagiotis Iosif Alexandra E. Koloniari Anastasios Tefas Nikolaos Passalis Francesco Salemi Gabriele Vedovato Sergey Klimenko Tanmaya Mishra Bernd Brügmann Elena Cuoco E. A. Huerta Chris Messenger Frank Ohme Max-Planck-Institut für Gravitationsphysik Albert-Einstein-Institut D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Friedrich-Schiller-Universität Jena D-07743 Jena Germany Michael Stifel Center Jena D-07743 Jena Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Astronomy Beijing Normal University Beijing 100875 China Peng Cheng Laboratory Shenzhen 518055 China Department of Informatics Aristotle University of Thessaloniki GR-54124 Thessaloniki Greece Department of Physics Aristotle University of Thessaloniki GR-54124 Thessaloniki Greece GSI Helmholtz Center for Heavy Ion Research Planckstraße 1 64291 Darmstadt Germany Università di Trento Dipartimento di Fisica I-38123 Povo Trento Italy INFN Trento Institute for Fundamental Physics and Applications I-38123 Povo Trento Italy INFN Sezione di Padova I-35131 Padova Italy Department of Physics University of Florida PO Box 118440 Gainesville Florida 32611-8440 USA European Gravitational Observatory (EGO) I-56021 Cascina Pisa Italy Scuola Normale Superiore Piazza dei Cavalieri 7 I-56126 Pisa Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 I-56127 Pisa Italy Data Science and Learning Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Computer Science University of Chicago Chicago Illinois 60637 USA SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ United Kingdom

We present the results of the first Machine Learning Gravitational-Wave Search Mock data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

关键词： Gravitational wave detection Gravitational waves Machine learning

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Why Are There Six Degrees of Separation in a Social Network?

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Physical Review X 2023年第2期13卷 021032-021032页

作者： I. Samoylenko D. Aleja E. Primo K. Alfaro-Bittner E. Vasilyeva K. Kovalenko D. Musatov A. M. Raigorodskii R. Criado M. Romance D. Papo M. Perc B. Barzel S. Boccaletti Moscow Institute of Physics and Technology 9 Institutskiy Pereulok Dolgoprudny Moscow Region 141701 Russia National Research University Higher School of Economics 6 Ulitsa Usacheva Moscow 119048 Russia Universidad Rey Juan Carlos Calle Tulipán s/n 28933 Móstoles Madrid Spain Department of Internal Medicine University of Michigan Ann Arbor Michigan 48109 USA Laboratory of Mathematical Computation on Complex Networks and their Applications Universidad Rey Juan Carlos Calle Tulipán s/n Móstoles 28933 Madrid Spain P.N. Lebedev Physical Institute of the Russian Academy of Sciences 53 Leninsky Prospekt Moscow 119991 Russia Scuola Superiore Meridionale Largo S. Marcellino 10 80138 Napoli NA Italy Russian Academy of National Economy and Public Administration 84 Prospekt Vernadskogo Moscow 119606 Russia Caucasus Mathematical Center at Adyghe State University 208 Pervomayskaya Ulitsa Maykop Adygea 385000 Russia Moscow State University 1 Leninskie Gory Moscow 119991 Russia Institute of Mathematics and Computer Science Buryat State University 5 Ulitsa Ranzhurova Ulan-Ude Buryatia 670000 Russia Data Complex Networks and Cybersecurity Research Institute Universidad Rey Juan Carlos Plaza Manuel Becerra 14 28028 Madrid Spain Department of Neuroscience and Rehabilitation University of Ferrara Ferrara Italy Center for Translational Neurophysiology of Speech and Communication Fondazione Istituto Italiano di Tecnologia Ferrara Italy Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 2000 Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung 404332 Taiwan Complexity Science Hub Vienna Josefstädterstraße 39 1080 Vienna Austria Alma Mater Europaea Slovenska ulica 17 2000 Maribor Slovenia Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Republic of Korea Department of Mathematics Bar-Ilan University Ramat-Gan 52900 Israel The Gonda Multi

A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.

关键词： Degree distributions Network evolution Real world networks Small-world networks

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QUATRE Algorithm with Sort Strategy for Global Optimization in Comparison with DE and PSO Variants 4th

QUATRE Algorithm with Sort Strategy for Global Optimization ...

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Pan, Jeng-Shyang Meng, Zhenyu Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

Optimization algorithm in swarm intelligence is getting more and more prevalent both in theoretical field and in real-world applications. Many nature-inspired algorithms in this domain have been proposed and employed in different applications. In this paper, a new QUATRE algorithm with sort strategy is proposed for global optimization. QUATRE algorithm is a simple but powerful stochastic optimization algorithm proposed in 2016 and it tackles the representational/positional bias existing in DE structure. Here a sort strategy is used for the enhancement of the canonical QUATRE algorithm. This advancement is verified on CEC2013 test suite for real-parameter optimization and also is contrasted with several state-of-the-art algorithms including Particle Swarm Optimization (PSO) variants, Differential Evolution (DE) variants on COCO framework under BBOB2009 benchmarks. Experiment results show that the proposed QUATRE algorithm with sort strategy is competitive with the contrasted algorithms. © 2018, Springer International Publishing AG.

关键词： Swarm intelligence

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Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

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NPJ digital medicine 2024年第1期7卷 240页

作者： Gregory Holste Mingquan Lin Ruiwen Zhou Fei Wang Lei Liu Qi Yan Sarah H Van Tassel Kyle Kovacs Emily Y Chew Zhiyong Lu Zhangyang Wang Yifan Peng Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA. Department of Surgery University of Minnesota Minneapolis MN USA. Center for Biostatistics and Data Science Washington University School of Medicine St. Louis MO USA. Department of Obstetrics & Gynecology Columbia University Irving Medical Center New York NY USA. Israel Englander Department of Ophthalmology Weill Cornell Medicine New York NY USA. Division of Epidemiology and Clinical Applications National Eye Institute National Institutes of Health (NIH) Bethesda MD USA. National Center for Biotechnology Information (NCBI) National Library of Medicine (NLM) National Institutes of Health (NIH) Bethesda MD USA. Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA. atlaswang@utexas.edu. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. yip4002@med.cornell.edu.

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The 2D Materials Roadmap

arXiv

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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

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Multi-view ontology alignment visualization (MOAV): A human-cognition based ontology alignment visualization technique

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 198-210页

作者： Chen, Jie Zhang, Jie Xue, Xingsi Huang, Yikun Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guangxi Guilin541004 China Department of Information Technology Concord University College Fujian Normal University No.68 Xueyuan Road University Town Minhou FuzhouFujian350117 China

To further improve the quality of ontology alignment, it is necessary for an ontology matcher to introduce a user’s knowledge into its automatic matching process, which yields the development of interactive ontology matching techniques. Since validating problematic entity correspondences is a difficult cognition task, user interaction based on Ontology Alignment Visualization (OAV) has become the critical component of an interactive ontology matcher, which directly affects the quality of validating result and the efficiency of validating process. The existing OAV tools are mainly developed based on their designers’ subjective feelings and experiences, which do not take into consideration the law of human cognition. To improve the efficiency of the ontology alignment validating process, in this paper, a Multi-view OAV (MOAV) is proposed, which synthetically utilizes the human cognitive theory-information visualization and human-computer inter-action. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test the performance of our proposal, and the experimental results show that MOAV can effectively improve a user’s validating efficiency. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Visualization

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A novel hybrid GWO-FPA algorithm for optimization applications 1st

A novel hybrid GWO-FPA algorithm for optimization applicatio...

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1st International Conference on Smart Vehicular Technology, Transportation, Communication and applications, VTCA 2017

作者： Pan, Jeng-Shyang Dao, Thi-Kien Chu, Shu-Chuan Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fuzhou China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia Department of Information Technology Haiphong Private University Haiphong Viet Nam

ISBN: (纸本)9783319707297

The recent trend of research is to hybridize two or several numbers of variants to find out the better quality of solution in practical optimization applications. In this paper, a new approach hybrid Grey Wolf Optimizer (GWO)-Flower Pollination Algorithm (FPA) is proposed based on the combination of exploitation phase in GWO and exploration stage in FPA. The hybrid proposed GWOFPA improves movement directions and speed of the grey wolves in updating positions of FPA. The simulation uses six benchmark tests for evaluating the performance of the proposed method. Compared other metaheuristics such as Particle Swarm Optimization (PSO), FPA, and GWO, the simulation results demonstrate that the proposed approach offers the better performance in solving optimization problems with or without unknown search areas. © Springer International Publishing AG 2018.

关键词： Particle swarm optimization (PSO)

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White paper on broadband connectivity in 6G

arXiv

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Björnson, Emil Bourdoux, André Buzzi, Stefano Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, MohammadJavad Sarieddeen, Hadi Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland Linköping University Sweden IMEC Belgium Department of Electrical and Information Engineering University of Cassino and Southern Latium Italy CEA-Leti France Department of Electrical and Electronics Engineering Kadir Has University Turkey Institute of Telecommunications and Multimedia Applications Universitat Politecnica de Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Department of Electrical and Computer Engineering Institute for the Wireless Internet of Things Northeastern University United States Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Department of Electrical Engineering Chalmers University of Technology Sweden Department of Electrical and Computer Engineering National University of Singapore Singapore Huawei Technologies Germany

This white paper explores the road to implementing broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, to support broadband connectivity at railway speeds up to 1000 km/h. To achieve these goals, not only the terrestrial networks will be evolved but they will also be integrated with satellite networks, all facilitating autonomous systems and various interconnected structures. We believe that several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric and scalable cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be used for pushing the peak rates of point-to-point links. The latter path will lead to THz communications complemented by visible light communications in specific scenarios. At the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latencies, higher reliability, and reduced complexity. Different options will be needed to optimally support different use cases. The resource efficiency can be further improved by using various combinations of full-duplex radios, interference management based on rate-splitting, machine-learning-based optimization, coded caching, and broadcasting. Finally, the three levels of enablers must be utilized not only to deliver better broadband services in urban areas, but also to provide full-coverage broadband connectivity must be one of the k

关键词： Visible light communication

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MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock data Challenge

arXiv

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arXiv 2022年

作者： Schäfer, Marlin Benedikt Zelenka, Ondřej Nitz, Alexander H. Wang, He Wu, Shichao Guo, Zong-Kuan Cao, Zhoujian Ren, Zhixiang Nousi, Paraskevi Stergioulas, Nikolaos Iosif, Panagiotis Koloniari, Alexandra E. Tefas, Anastasios Passalis, Nikolaos Salemi, Francesco Vedovato, Gabriele Klimenko, Sergey Mishra, Tanmaya Brügmann, Bernd Cuoco, Elena Huerta, E.A. Messenger, Chris Ohme, Frank Max-Planck-Institut für Gravitationsphysik Albert-Einstein-Institut HannoverD-30167 Germany Leibniz Universität Hannover HannoverD-30167 Germany Friedrich-Schiller-Universität Jena JenaD-07743 Germany Michael Stifel Center Jena JenaD-07743 Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Department of Astronomy Beijing Normal University Beijing100875 China Peng Cheng Laboratory Shenzhen518055 China Department of Informatics Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Department of Physics Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece GSI Helmholtz Center for Heavy Ion Research Planckstraße 1 Darmstadt64291 Germany Università di Trento Dipartimento di Fisica Povo TrentoI-38123 Italy INFN Trento Institute for Fundamental Physics and Applications Povo TrentoI-38123 Italy INFN Sezione di Padova PadovaI-35131 Italy Department of Physics University of Florida PO Box 118440 GainesvilleFL32611-8440 United States Cascina PisaI-56021 Italy Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 PisaI-56127 Italy Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States SUPA School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

We present the results of the first Machine Learning Gravitational-Wave Search Mock data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥ 200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: An Overview 4th

The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm...

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Meng, Zhenyu Pan, Jeng-Shyang Li, Xiaoqing Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319685267

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a new simple but powerful stochastic optimization algorithm proposed recently. The QUATRE algorithm aims to tackle the representational/positional bias inborn with DE algorithm and secures an overall better performance on commonly used Conference of Evolutionary Computation (CEC) benchmark functions. Recently, several QUATRE variants have been already proposed since its inception in 2016 and performed very well on many benchmark functions. In this paper, we mainly have a brief overview of all these proposed QUATRE variants first and then make simple contrasts between these QUATRE variants and several state-of-the-art DE variants under CEC2013 test suites for real-parameter single objective optimization benchmark functions. Experiment results show that the movement trajectory of individuals in the QUATRE structure is much more efficient than DE structure on most of the tested benchmark functions. © 2018, Springer International Publishing AG.

关键词： Global optimization

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