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arXiv 2023年

作者： Karimi, Mehdi Department of Mathematics Illinois State University NormalIL61761 United States

This paper proposes group-based distributed optimization (DO) algorithms on top of intelligent partitioning for the optimal power flow (OPF) problems. Radial partitioning of the graph of a network is introduced as a systematic way to split a large-scale problem into more tractable sub-problems, which can potentially be solved efficiently with methods such as convex relaxations. Spectral parameter selection is introduced for group-based DO as a crucial hyper-parameter selection step in DO. A software package DiCARP is created, which is implemented in Python using the Pyomo optimization package. Our numerical results for different power network instances show that our designed algorithm returns more accurate solutions to the tested problems with fewer iterations than component-based DO. Our results confirm the importance of smart partitioning and parameter selection for large-scale optimization problems on networks. © 2023, CC BY-NC-SA.

关键词： optimization algorithms

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Power loss minimization and voltage profile improvement by distributed generation(DG) sizing and placement

Power loss minimization and voltage profile improvement by d...

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IEEE PES/IAS PowerAfrica Conference

作者： Ntombela, Mlungisi Musasa, Kabeya Leoaneka, Moketjema Clarence Durban Univ Technol Dept Elect Power Engn Fac Engn & Built Environm Durban South Africa

ISBN: (数字)9781665466394

ISBN: (纸本)9781665466394

In order to reduce power losses in a power system and to improve voltage profile, researchers have examined and studied optimizing Photovoltaic Power distributed generation (DG) which is location and size in a power system, but the results have some drawbacks. Several approaches developed to make this important issue more efficient, including coming up with new algorithms and improving those already in existence. Many of the proposed algorithms are only concerned with the real power loss, however. Voltage stability control is a critical factor in modern power systems, which makes incorporating reactive power losses in optimizing DG allocation for voltage profile improvement necessary. The goal of this work is to solve this issue by combining Genetic Algorithm and Improved Particle Swarm optimization to optimize DG size and location by considering both real and reactive power losses. Power loss sensitivity factors and real and reactive power flow factors used in identifying which buses will receive DGs. A MATLAB- based program was developed and tested on a test system using distributed generators, considering the proposed method. As compared to Genetic Algorithm, Particle Swarm optimization and Improved Particle Swarm optimization methods, the Hybrid Genetic Algorithm Improved Particle Swarm optimization method is better for reducing both real and reactive power losses.

关键词： Power loss minimization Power system reconfiguration Voltage profile improvement optimization algorithms

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An Algorithm for Computing the Stratonovich's Value of Information 17

An Algorithm for Computing the Stratonovich's Value of Infor...

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17th International Symposium on Information Theory and its Applications (ISITA)

作者： Kamatsuka, Akira Yoshida, Takahiro Kazama, Koki Matsushima, Toshiyasu Shonan Inst Technol Fujisawa Kanagawa Japan Nihon Univ Tokyo Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9784885523410

We propose an algorithm for computing Stratonovich's value of information (VoI) that can be regarded as an analog of the distortion-rate function. We construct an alternating optimization algorithm for VoI under a general information leakage constraint and derive a convergence condition. Furthermore, we discuss algorithms for computing VoI under specific information leakage constraints, such as Shannon's mutual information (MI), f-leakage, Arimoto's MI, Sibson's MI, and Csiszar's MI. A full version of this paper [1] is accessible at: https://***/abs/2205.02778

关键词： optimization algorithms

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An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

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IEEE ACCESS 2021年 9卷 21843-21858页

作者： Zhou, Anan Yi, Benshun Liu, Yusheng Luo, Laigan Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China

To reduce recovery cost of repairing multiple failed nodes, many repair schemes have been proposed for erasure codes based distributed storage systems. However, most of the existing researches ignore the network topology of storage devices. Motivated by such considerations, we combine delay repair schemes with network topology and propose a tree-structured model based on fountain codes with large value of {n,k,r}to improve the repair efficiency. More precisely, with the consideration of network topology, a new target named data recovery cost is defined to measure the efficiency of coded fragment download and source file reconstruction, and then the optimal recovery threshold is derived to minimize the average data recovery cost of general tree-structured model. Moreover, we analyze and compare the average data recovery cost of general tree-structure with different systematic parameters. To further improve the data transmission efficiency, an optimal tree-structured scheme based on improved tabu search algorithm (ITSA-ORT) is proposed. Compared with other algorithms, the ITSA-ORT scheme uses Prim algorithm to generate the initial solution and then uses special method to obtain the corresponding neighborhood structure. The experimental results show that the proposed scheme can find a globally optimal solution and obtain lower cost of data recovery. In addition, the ITSA-ORT scheme has lower computational complexity than the optimal tree-structured scheme based on particle swarm optimization algorithm (PSO-ORT) and the optimal tree-structured scheme based on firefly algorithm (FA-ORT).

关键词： Centralized communication model fountain codes delay repair network topology tree-structured model optimization algorithms

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Time efficiency in optimization with a bayesian-Evolutionary algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 100970-100970页

作者： Lan, Gongjin Tomczak, Jakub M. Roijers, Diederik M. Eiben, A. E. Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Guangdong Peoples R China Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Vrije Univ Brussel AI Lab Brussels Belgium HU Univ Appl Sci Utrecht Inst ICT Utrecht Netherlands

Not all generate-and-test search algorithms are created equal. Bayesian optimization (BO) invests a lot of computation time to generate the candidate solution that best balances the predicted value and the uncertainty given all previous data, taking increasingly more time as the number of evaluations performed grows. Evolutionary algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time. Both BO and EA community typically assess their performance as a function of the number of evaluations, i.e., data efficiency. However, this is unfair once we start to compare the efficiency of these classes of algorithms, as the overhead times to generate candidate solutions are significantly different . We suggest to measure the efficiency of generate-and-test search algorithms as the expected gain in the objective value per unit of computation time spent, i.e., time efficiency. To the time-efficient search algorithm, we therefore propose a new algorithm, a combination of BO and an EA, BEA for short, that starts with BO, then transfers knowledge to an EA, and subsequently runs the EA. We compare the BEA with BO, the EA, Differential Evolution (DE), and Particle Swarm optimization (PSO). The results show that BEA outperforms BO, the EA, DE and PSO in terms of time efficiency, and ultimately leads to better performance on well-known benchmark objective functions with many local optima. Moreover, we test BEA, BO, and the EA on nine test cases of robot learning problems and here again we find that BEA outperforms the other algorithms.

关键词： optimization algorithms Time efficiency Bayesian optimization Evolutionary algorithm Differential evolution Particle swarm optimization Evolutionary robotics

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Exponential Quantum Speedup for Simulation-Based optimization Applications

arXiv

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arXiv 2023年

作者： Stein, Jonas Müller, Lukas Hölscher, Leonhard Chnitidis, Georgios Jojo, Jezer Farea, Afrah Çelebi, Mustafa Serdar Bucher, David Wulf, Jonathan Fischer, David Altmann, Philipp Linnhoff-Popien, Claudia Feld, Sebastian Institute for Informatics LMU Munich Germany Aqarios GmbH Munich Germany BMW Group Munich Germany Indian Institute of Science Education and Research Pune India Istanbul Technical University Turkey Quantum & Computer Engineering Delft University of Technology Netherlands

The simulation of many industrially relevant physical processes can be executed up to exponentially faster using quantum algorithms. However, this speedup can only be leveraged if the data input and output of the simulation can be implemented efficiently. While we show that recent advancements for optimal state preparation can effectively solve the problem of data input at a moderate cost of ancillary qubits in many cases, the output problem can provably not be solved efficiently in general. By acknowledging that many simulation problems arise only as a subproblem of a larger optimization problem in many practical applications however, we identify and define a class of practically relevant problems that does not suffer from the output problem: Quantum Simulation-based optimization (QuSO). QuSO represents optimization problems whose objective function and/or constraints depend on summary statistic information on the result of a simulation, i.e., information that can be efficiently extracted from a quantum state vector. In this article, we focus on the LinQuSO subclass of QuSO, which is characterized by the linearity of the simulation problem, i.e., the simulation problem can be formulated as a system of linear equations. By cleverly combining the quantum singular value transformation (QSVT) with the quantum approximate optimization algorithm (QAOA), we prove that a large subgroup of LinQuSO problems can be solved with up to exponential quantum speedups with regards to their simulation component. Finally, we present two practically relevant use cases that fall within this subgroup of QuSO problems. © 2023, CC BY.

关键词： optimization algorithms

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Arithmetic optimization approach for parameters identification of different PV diode models with FOPI-MPPT

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AIN SHAMS ENGINEERING JOURNAL 2022年第3期13卷

作者： Mahmoud, Mahmoud F. Mohamed, Ahmed T. Swief, R. A. Said, Lobna A. Radwan, Ahmed G. Nile Univ Nanoelect Integrated Syst Ctr NISC Giza Egypt Ain Shams Univ Fac Engn Cairo Egypt Cairo Univ Fac Engn Engn Math & Phys Dept Giza 12613 Egypt Nile Univ Sch Engn & Appl Sci Giza Egypt

The Maximum Power Point Tracker (MPPT) provides the most efficient use of a Photo-voltaic system independent of irradiance or temperature fluctuations. This paper introduces the modeling and control of a photo-voltaic system operating at MPPT using the arithmetic optimization algorithm (AOA). The single and double Photo-voltaic models are investigated. Their optimal unknown parameters are extracted using AOA based on commercial Photo-voltaic datasheets. A comparison is performed between these optimal parameters extracted by AOA and other optimization techniques presented in the literature. These parameters generate the P - V and I - V curves for the studied models considering the temperature factor. A good match is achieved relative to the manufacturer data. A DC-DC boost converter is used as a link between the PV modules and the load. The converter duty cycle is adjusted, varying the climatic conditions using three cases: without a controller, using PI controller, and using the fractional-order PI controller (FOPI). The AOA is employed to set the optimum controllers parameters to maintain the impedance matching between the PV modules and the load. The FOPI shows a significant improvement in controlling the system performance.(c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： Photo-voltaic Boost converter optimization algorithms AOA MPPT algorithms Fractional order circuits FOPID PID

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FINITE ADAPTABILITY IN TWO-STAGE ROBUST optimization: ASYMPTOTIC OPTIMALITY AND TRACTABILITY

arXiv

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arXiv 2023年

作者： Kedad-Sidhoum, Safia Medvedev, Anton Meunier, Frédéric CEDRIC CNAM France CEDRIC CNAM & SGR France CERMICS École nationale des ponts et chaussées France

Two-stage robust optimization is a fundamental paradigm for modeling and solving optimization problems with uncertain parameters. A now classical method within this paradigm is finite adaptability, introduced by Bertsimas and Caramanis (IEEE Transactions on Automatic Control, 2010). It consists in restricting the recourse to a finite number k of possible values. In this work, we point out that the continuity assumption they stated to ensure the convergence of the method when k goes to infinity is not correct, and we propose an alternative assumption for which we prove the desired convergence. Bertsimas and Caramanis also established that finite adaptability is NP-hard, even in the special case when k = 2, the variables are continuous, and only specific parameters are subject to uncertainty. We provide a theorem showing that this special case becomes polynomial when the uncertainty set is a polytope with a bounded number of vertices, and we extend this theorem for k = 3 as well. On our way, we establish new geometric results on coverings of polytopes with convex sets, which might be interesting for their own *** Codes 90C17 Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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A linear optimization oracle for zonotope computation

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COMPUTATIONAL GEOMETRY-THEORY AND APPLICATIONS 2022年 100卷 101809-101809页

作者： Deza, Antoine Pournin, Lionel McMaster Univ Hamilton ON Canada Ecole Polytech CNRS LIX Palaiseau France Univ Paris 13 LIPN Villetaneuse France

A class of counting problems asks for the number of regions of a central hyperplane arrangement. By duality, this is the same as counting the vertices of a zonotope. Efficient algorithms are known that solve this problem by computing the vertices of a zonotope from its set of generators. Here, we give an efficient algorithm, based on a linear optimization oracle, that performs the inverse task and recovers the generators of a zonotope from its set of vertices. We also provide a variation of that algorithm that allows to decide whether a polytope, given as its vertex set, is a zonotope and when it is not a zonotope, to compute its greatest zonotopal summand. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Enumeration algorithms Hyperplane arrangements Minkowski sums optimization algorithms

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Lightning search algorithm: a comprehensive survey

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APPLIED INTELLIGENCE 2021年第4期51卷 2353-2376页

作者： Abualigah, Laith Abd Elaziz, Mohamed Hussien, Abdelazim G. Alsalibi, Bisan Jalali, Seyed Mohammad Jafar Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Zagazig Univ Dept Math Fac Sci Zagazig Egypt Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Fayoum Univ Fac Sci Al Fayyum Egypt Univ Sains Malaysia Sch Comp Sci USM George Town 11800 Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Waurm Ponds Vic 3216 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA's applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper.

关键词： Lightning search algorithm (LSA) Meta-heuristics optimization algorithms optimization problems

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