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作者： Jie Liu Lehigh University

学位级别：博士

In this thesis, we discuss and develop randomized algorithms for big data problems. In particular, we study the finite-sum optimization with newly emerged variance- reduction optimization methods (Chapter 2), explore the efficiency of second-order information applied to both convex and non-convex finite-sum objectives (Chapter 3) and employ the fast first-order method in power system problems (Chapter 4). In Chapter 2, we propose two variance-reduced gradient algorithms – mS2GD and SARAH. mS2GD incorporates a mini-batching scheme for improving the theoretical complexity and practical performance of SVRG/S2GD, aiming to minimize a strongly convex function represented as the sum of an average of a large number of smooth con- vex functions and a simple non-smooth convex regularizer. While SARAH, short for StochAstic Recursive grAdient algoritHm and using a stochastic recursive gradient, targets at minimizing the average of a large number of smooth functions for both con- vex and non-convex cases. Both methods fall into the category of variance-reduction optimization, and obtain a total complexity of O ((n + κ)log(1/ε)) to achieve an ε -accuracy solution for strongly convex objectives, while SARAH also maintains a sub-linear convergence for non-convex problems. Meanwhile, SARAH has a practical variant SARAH+ due to its linear convergence of the expected stochastic gradients in inner loops. In Chapter 3, we declare that randomized batches can be applied with second- order information, as to improve upon convergence in both theory and practice, with a framework of L-BFGS as a novel approach to finite-sum optimization problems. We provide theoretical analyses for both convex and non-convex objectives. Meanwhile, we propose LBFGS-F as a variant where Fisher information matrix is used instead of Hessian information, and prove it applicable to a distributed environment within the popular applications of least-square and cross-entropy losses. In Chapter 4, we develop fast rand

关键词： big data optimization Large-scale optimization Machine Learning Power Systems Randomized Methods

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Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems

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APPLIED SOFT COMPUTING 2020年 87卷

作者： Abdi, Yousef Feizi-Derakhshi, Mohammad-Reza Univ Tabriz Fac Elect & Comp Engn Computerized Intelligence Syst Lab Tabriz Iran

big data optimization (big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for big-Opt problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： big data optimization Hybrid multi-objective evolutionary algorithm Search Manager framework Evolutionary operators

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Chaotic golden ratio guided local search for big data optimization

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2023年 41卷

作者： Kocer, Havva Gul Turkoglu, Bahaeddin Uymaz, Sait Ali Selcuk Univ Konya Turkiye Nigde Omer Halisdemir Univ Dept Comp Engn Nigde Turkiye Konya Tech Univ Dept Comp Engn Konya Turkiye

Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition based optimization problems, named as big data optimization problem (big-Opt), introduced at the Congress on Evolutionary Computing big data Competition (CEC'2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.& COPY;2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： big data optimization Local search Golden ratio Chaotic map Memetic algorithm

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An optimal fog-cloud offloading framework for big data optimization in heterogeneous IoT networks

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Decision Analytics Journal 2023年 8卷

作者： Bebortta, Sujit Tripathy, Subhranshu Sekhar Modibbo, Umar Muhammad Ali, Irfan Department of Computer Science Ravenshaw University Odisha Cuttack 753003 India School of Computer Engineering KIIT Deemed to be University Campus 15 Rd Chandaka Industrial Estate Odisha Patia Bhubaneswar 751024 India Department of Operations Research Modibbo Adama University Yola P.M.B. 2076 Nigeria Department of Statistics and Operations Research Aligarh Muslim University Aligarh 202 002 India

Executing complex and time-sensitive operations has become difficult due to the increased acceptance of Internet of Things (IoT) devices and IoT-generated big data, which can result in problems with power consumption and lag time. The fog computing layer is a distributed computing option that can handle some of these issues. However, fog computing devices’ limited computing and power capabilities make it difficult to complete operations under time constraints while minimizing service latency and fog resource energy consumption in delay-sensitive IoT-Fog applications. This paper suggests a dynamic integer linear programming technique for facilitating optimal task offloading that distributes resources from the fog computing layer to IoT devices while considering the constraints on timely task execution and resource availability. The offloading challenge is modeled as an integer linear programming (ILP) problem to ease the burden on finite fog resources and speed up the completion of time-sensitive operations. Given the large dimensionality of tasks in a dynamic environment, a task prioritization method is adopted to minimize tasks’ latency and the energy consumption of fog nodes. The findings demonstrate that the suggested approach performs better than benchmark approaches regarding energy utilization and service latency. Overall, the proposed technique offers an efficient and practical response to the issues raised by IoT devices and fog computing while enhancing system efficiency regarding power consumption and latency. © 2023 The Author(s)

关键词： big data optimization Computation offloading Energy consumption Fog computing Integer linear programming Internet of Things

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A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization

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Journal of Advanced Research 2024年

作者： Liu, Bo Zhou, Yongquan Wei, Yuanfei Luo, Qifang College of Artificial Intelligence Guangxi University for Nationalities Nanning 530006 China Xiangsihu College Guangxi University for Nationalities Nanning 530225 China Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning 530006 China

Introduction: big data optimization (big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient. Objectives: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve big-Opt problem. Methods: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm;shift density estimation is introduced as a mechanism for providing selection pressure for population evolution;and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to big-Opt problems. Results: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms. Conclusion: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data. © 2024

关键词： African vulture optimization algorithm (AVOA) big data optimization Binary hierarchical structure Metaheuristic Multi-objective African vultures optimization algorithm (MO_Tree_BHSAVOA) Tree topology

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Behavior of crossover operators in NSGA-III for large-scale optimization problems

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INFORMATION SCIENCES 2020年 509卷 470-487页

作者： Yi, Jiao-Hong Xing, Li-Ning Wang, Gai-Ge Dong, Junyu Vasilakos, Athanasios V. Alavi, Amir H. Wang, Ling Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Shandong Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Shandong Peoples R China Lulea Univ Technol Dept Comp Sci Elect & Space Engn SE-93187 Skelleftea Sweden Univ Missouri Dept Civil & Environm Engn Columbia MO 65201 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Electroencephalography Large-scale optimization big data optimization Evolutionary multi-objective optimization NSGA-III Crossover operator Performance analysis

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big data optimisation and management in supply chain management: a systematic literature review

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 1期56卷 253-284页

作者： Alsolbi, Idrees Shavaki, Fahimeh Hosseinnia Agarwal, Renu Bharathy, Gnana K. Prakash, Shiv Prasad, Mukesh Umm Al Qura Univ Coll Comp Sci & Informat Syst Dept Informat Syst Mecca Saudi Arabia Univ Technol Sydney Sch Comp Sci Ultimo Australia Univ Technol Sydney Business Sch Ultimo Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia Univ Allahabad Dept Elect & Commun Prayagraj Uttar Pradesh India

The increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.

关键词： big data big data optimization big data management Supply chain management Performance measurement Systematic review

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Optimizing big data Processing in Cloud by Integrating Versatile Front End to database Systems

Optimizing Big Data Processing in Cloud by Integrating Versa...

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IEEE International Conference on Energy Systems and Applications (ICESA)

作者： Myalapalli, Vamsi Krishna Totakura, Thirumala Padmakumar Open Text Corp Mind Space IT Pk Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467368179

The magnitude of data in the concurrent databases is exponentially escalating with respect to time. This advent conveys a challenge in the arena of performance, due to incessant data accumulation, manipulation, analyzing and processing massive volumes of data. The rising space among users and data, invokes inventive tool(s) for resolving the challenges tackled by velocity, volume and variety of big data. This paper introduces a novel and versatile Front End Module an Entry Control Module integrated to the database System(s) in Cloud environments to minimize the impact on underlying database(s). In a nutshell this module aims at performance engineering at database level, so that the saved band width could be used to process other requests arriving database. Our experimental approaches and progression evinced that response time is reduced and bandwidth is preserved. Comprehensively, the impact on database(s) is/are minimized

关键词： big data optimization big data Response Time Tuning Query Tuning Query Rewrite database Front End Cloud database optimization

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The application of big data analytics in optimizing logistics: a developmental perspective review

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Journal of data, Information and Management 2019年第1-2期1卷 33-43页

作者： Yan, Zengwen Ismail, Hossam Chen, Lujie Zhao, Xiande Wang, Liang Xi’an Jiaotong-Liverpool University Suzhou China China Europe International Business School Shanghai China

This paper adopts a developmental perspective to review articles and reports published in the past decade on the application of big data to the optimization of logistics. First, the evolution and features of both logistics and big data are reviewed using the systematic review method. This is followed by discussions on the implementation of big data in logistics and the optimization outcomes. The paper summarizes the four main effects of the adoption of big data in logistics: informatization;operation efficiency;service quality;and the promotion of technical upgrading. © Springer Nature Switzerland AG 2019.

关键词： big data application big data optimization Logistics evolution Logistics optimization

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Distributed Block Coordinate Descent for Minimizing Partially Separable Functions 3rd

Distributed Block Coordinate Descent for Minimizing Partiall...

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3rd International Conference on Numerical Analysis and optimization - Theory, Methods, Applications and Technology Transfer ((NAO)

作者： Marecek, Jakub Richtarik, Peter Takac, Martin IBM Res Ireland Dublin Ireland Univ Edinburgh Sch Math Edinburgh Midlothian Scotland Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA

ISBN: (纸本)9783319176895;9783319176888

A distributed randomized block coordinate descent method for minimizing a convex function of a huge number of variables is proposed. The complexity of the method is analyzed under the assumption that the smooth part of the objective function is partially block separable. The number of iterations required is bounded by a function of the error and the degree of separability, which extends the results in Richtarik and Takac (Parallel Coordinate Descent Methods for big data optimization, Mathematical Programming, DOI: 10.1007/s10107-0150901-6) to a distributed environment. Several approaches to the distribution and synchronization of the computation across a cluster of multi-core computer are described and promising computational results are provided.

关键词： Distributed coordinate descent Empirical risk minimization Support vector machine big data optimization Partial separability Huge-scale optimization Iteration complexity Expected separable over-approximation Composite objective Convex optimization Communication complexity

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