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On the Convergence of Nested Decentralized Gradient Methods With Multiple Consensus and Gradient Steps

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4192-4203页

作者： Berahas, Albert S. Bollapragada, Raghu Wei, Ermin Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48019 USA Univ Texas Austin Operat Res & Ind Engn Program Austin TX 78712 USA Northwestern Univ Dept Ind Engn Management Sci Dept Elect & Comp Engn Evanston IL 60208 USA

In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where the cost of communication and/or computation can be expensive. We extend and generalize the analysis for a class of nested gradient-based distributed algorithms [NEAR-DGD, (Berahas et al., 2019)] to account for multiple gradient steps at every iteration. We show the effect of performing multiple gradient steps on the rate of convergence and on the size of the neighborhood of convergence, and prove R-Linear convergence to the exact solution with a fixed number of gradient steps and increasing number of consensus steps. We test the performance of the generalized method on quadratic functions and show the effect of multiple consensus and gradient steps in terms of iterations, number of gradient evaluations, number of communications and cost.

关键词： Convergence optimization Linear programming Signal processing algorithms Symmetric matrices Eigenvalues and eigenfunctions Convex functions Distributed optimization communication optimization algorithms network optimization

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From optimization to Control: Quasi Policy Iteration

arXiv

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arXiv 2023年

作者： Sharifi Kolarijani, Mohammad Amin Esfahani, Peyman Mohajerin Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Recent control algorithms for Markov decision processes (MDPs) have been designed using an implicit analogy with well-established optimization algorithms. In this paper, we review this analogy across four problem classes with a unified solution characterization allowing for a systematic transformation of algorithms from one domain to the other. In particular, we identify equivalent optimization and control algorithms that have already been pointed out in the existing literature, but mostly in a scattered way. With this unifying framework in mind, we adopt the quasi-Newton method from convex optimization to introduce a novel control algorithm coined as quasi-policy iteration (QPI). In particular, QPI is based on a novel approximation of the "Hessian" matrix in the policy iteration algorithm by exploiting two linear structural constraints specific to MDPs and by allowing for the incorporation of prior information on the transition probability kernel. While the proposed algorithm has the same computational complexity as value iteration, it interestingly exhibits an empirical convergence behavior similar to policy iteration with a very low sensitivity to the discount factor. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Systematic Approach for Generation of Feasible Deployment Alternatives for Microservices

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IEEE ACCESS 2021年 9卷 29505-29529页

作者： Aksakalli, Isil Karabey Celik, Turgay Can, Ahmet Burak Tekinerdogan, Bedir Erzurum Tech Univ Dept Comp Engn TR-25050 Erzurum Turkey MilSOFT Software Technol Corp TR-06800 Ankara Turkey Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey Wageningen Univ & Res Informat Technol Grp NL-6706 Wageningen Netherlands

Microservice architectures rely on the development of modular and independent software units, which typically address a single task and communicate with other microservices via well-defined interfaces. This has several benefits such as easier maintenance and update of services. However, deploying a microservice-based application is often more complicated than a monolithic application. While a monolithic application can be deployed one at a time on a group of similar servers behind a load balancer, a microservice-based application consists of different microservices and each microservice usually has more than one runtime instance that needs to be configured and deployed. For a small number of microservices and applications, the deployment could be done manually. However, a large number of microservices are frequently observed in practice. In such cases, the deployment becomes cumbersome and error-prone and does not scale with the increased number of services. To cope with this problem, we present a systematic approach and the corresponding tool support for enabling the deployment of microservices to resources that have limited capacity. Hereby, we model and define the design space given the deployment parameters and automatically derive the feasible deployment solution. The approach is validated using a taxi-hailing system case study inspired by Uber which has spread all over the world in recent years.

关键词： Containers Computer architecture Cloud computing Servers Scalability Task analysis Tools Automated deployments for microservices capacitated task assignment problem (CTAP) deriving feasible microservice deployments optimization algorithms

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Exploiting the Moth-Flame optimization Algorithm for Optimal Load Management of the University Campus: A Viable Approach in the Academia Sector

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ENERGIES 2022年第10期15卷 3741页

作者： Ullah, Ibrar Hussain, Irshad Rehman, Khalid Wroblewski, Piotr Lewicki, Wojciech Kavin, Balasubramanian Prabhu Univ Engn & Technol Fac Elect & Comp Engn Peshawar 25000 Pakistan CECOS Univ Fac Elect Engn Peshawar 25000 Pakistan Univ Technol & Econ H Chodkowska Warsaw Fac Engn Jutrzenki 135 PL-02231 Warsaw Poland Mil Univ Technol Fac Mechatron Armament & Aerosp Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland West Pomeranian Univ Technol Szczecin Fac Econ Zolnierska 47 PL-71210 Szczecin Poland Sri Ramachandra Inst Higher Educationand Res Sri Ramachandra Fac Engn & Technol Chennai 60011 Tamil Nadu India

Unbalanced load condition is one of the major issues of all commercial, industrial and residential sectors. Unbalanced load means that, when different loads are distributed on a three-phase four-wire system, unequal currents pass through the three phases. Due to it, a heavy current flows in the neutral wire, which not only adds the losses, but also puts constraints on three phases' loads. In this paper, we have presented a practical approach for load balancing. First, we have considered the existing three-phase load system where the supply is a three-phase unbalanced supply. Before balancing the load, it is necessary to compensate the current in neutral wire. A nature-inspired moth-flame optimization (MFO) algorithm is used to propose a scheme for balancing of current in neutral wire. The information of a distributed single-phase load was used to balance the currents in a three-phase system. The feeder phase and load profiles of each single-phase load are used to reconfigure the network using an optimization process. By balancing the current of three phases, the current of the neutral conductor in substation transformers was reduced to almost zero.

关键词： load balancing moth-flame optimization (MFO) algorithm neutral current reduction optimization algorithms practical approach of optimization

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Achieving geometric convergence for distributed optimization with Barzilai-Borwein step sizes

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Science China(Information Sciences) 2022年第4期65卷 280-281页

作者： Juan GAO Xin-Wei LIU Yu-Hong DAI Yakui HUANG Peng YANG School of Artificial Intelligence Hebei University of Technology Institute of Mathematics Hebei University of Technology LSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

Dear editor,We consider distributed optimization, which can be formulated to minimize the average of all local objective functions:■where each local function fi:■ is Li-smooth and μi-strongly convex and known only ... 详细信息

关键词： distributed optimization multi-agent network Barzilai-Borwein step sizes optimization algorithms convergence rate

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Guided Intelligent Hyper-Heuristic Algorithm for Critical Software Application Testing Satisfying Multiple Coverage Criteria

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第2期33卷 2450029-2450029页

作者： Rani, S. Alagu Akila, C. Raja, S. P. Anna Univ Reg Campus Dept Comp Sci & Engn Tirunelveli 627007 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods.

关键词： optimization algorithms hyper-heuristic artificial intelligence software testing fault detection

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GBO: A Multi-Granularity optimization Algorithm via Granular-ball for Continuous Problems

arXiv

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arXiv 2023年

作者： Xia, Shuyin Lin, Xinyu Wang, Guan Chen, De-Gang Zhao, Sen Wang, Guoyin Liang, Jing Chongqing Key Laboratory of Computational Intelligence China Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Chongqing China Chongqing Normal University China National Center for Applied Mathematics in Chongging Chongging Normal University Chongging401331 China Zhengzhou University China

optimization problems aim to find the optimal solution, which is becoming increasingly complex and difficult to solve. Traditional evolutionary optimization methods always overlook the granular characteristics of solution space. In the real scenario of numerous optimizations, the solution space is typically partitioned into sub-regions characterized by varying degree distributions. These sub-regions present different granularity characteristics at search potential and difficulty. Considering the granular characteristics of the solution space, the number of coarse-grained regions is smaller than the number of points, so the calculation is more efficient. On the other hand, coarse-grained characteristics are not easily affected by fine-grained sample points, so the calculation is more robust. To this end, this paper proposes a new multi-granularity evolutionary optimization method, namely the Granular-ball optimization (GBO) algorithm, which characterizes and searches the solution space from coarse to fine. Specifically, using granular-balls instead of traditional points for optimization increases the diversity and robustness of the random search process. At the same time, the search range in different iteration processes is limited by the radius of granular-balls, covering the solution space from large to small. The mechanism of granular-ball splitting is applied to continuously split and evolve the large granular-balls into smaller ones for refining the solution space. Extensive experiments on commonly used benchmarks have shown that GBO outperforms popular and advanced evolutionary algorithms. The code can be found in the supporting materials. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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An Improved Fuzzy Adaptive Firefly Algorithm-Based Hybrid Clustering algorithms

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2021年第SUPPL 2期29卷 259-278页

作者： Agrawal, Anmol Tripathy, B. K. Thirunavukarasu, Ramkumar Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms.

关键词： Data clustering Firefly algorithm fuzzy adaptive firefly fuzzy adaptive fuzzy firefly optimization algorithms soft clustering

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Optimizing Extensibility of CAN FD for Automotive Cyber-Physical Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第12期22卷 7875-7886页

作者： Xie, Yong Zeng, Gang Kurachi, Ryo Xiao, Fu Takada, Hiroaki Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Nagoya Univ Grad Sch Engn Nagoya Aichi 4648603 Japan Nagoya Univ Grad Sch Informat Sci Nagoya Aichi 4648603 Japan

Extensibility is an important optimization objective for the E/E architecture of automotive cyber-physical systems (ACPS), while little attention has paid to the extensibility-aware design of in-vehicle network. To address this problem, this paper formulates a trade-off problem that balances the bandwidth utilization and the extensibility from the initial design of CAN FD. We firstly propose a new extensibility model and the related evaluation metric, and then two optimization algorithms, namely, the mixed integer linear programming (MILP) approach and the simulated annealing (SA) based heuristic approach, are proposed to resolve the trade-off problem for mid-sized and industry sized signal sets, respectively. The experiment results show the efficiency of the proposed extensibility metric and the optimization algorithms. By comparing with state-of-the-art algorithm, the MILP reduces the increase range of the bandwidth utilization of the extended signal set by 18.17% to 57.64% averagely, and 49.22% to 89.40% maximally, with only 0.06% to 0.79% bandwidth utilization overhead;the SA approach can reduces the increase range of the bandwidth utilization of the extended signal set by 12.71% to 58.33% averagely, and 40.08% to 89.40% maximally, with only 0.06% to 0.8% bandwidth utilization overhead.

关键词： Bandwidth Payloads Heuristic algorithms optimization Measurement Task analysis Uncertainty Automotive cyber-physical systems CAN FD signal packing bandwidth utilization extensibility optimization algorithms

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A Review of Modern Computational Techniques and Their Role in Power System Stability and Control

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ENERGIES 2024年第1期17卷 177页

作者： Pavon, Wilson Jaramillo, Manuel Vasquez, Juan C. Univ Politecn Salesiana Engn Dept Quito 170525 Ecuador Univ Politecn Salesiana Dept Elect Engn Quito 170525 Ecuador Aalborg Univ AAU Energy Dept Ctr Res Microgrids CROM DK-9220 Aalborg Denmark

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks.

关键词： powersystems stability computational techniques renewable energy integration real-time system control data management cybersecurity in power networks machine learning applications control strategies system modeling efficiency optimization algorithms

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