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arXiv 2023年

作者： Wu, Kai Li, Xiaobin Liu, Penghui Liu, Jing Guangzhou Institute of Technology Xidian University Guangzhou510555 China School of Artificial Intelligence Xidian University Xi’an710071 China

Evolutionary algorithms (EAs) have emerged as a powerful framework for optimization, especially for black-box optimization. Existing evolutionary algorithms struggle to comprehend and effectively utilize task-specific information for adjusting their optimization strategies, leading to subpar performance on target tasks. Moreover, optimization strategies devised by experts tend to be highly biased. These challenges significantly impede the progress of the field of evolutionary computation. Therefore, this paper first introduces the concept of Automated EA: Automated EA exploits structure in the problem of interest to automatically generate update rules (optimization strategies) for generating and selecting potential solutions so that it can move a random population near the optimal solution. However, current EAs cannot achieve this goal due to the poor representation of the optimization strategy and the weak interaction between the optimization strategy and the target task. We design a deep evolutionary convolution network (DECN) to realize the move from hand-designed EAs to automated EAs without manual interventions. DECN has high adaptability to the target task and can obtain better solutions with less computational cost. DECN is also able to effectively utilize the low-fidelity information of the target task to form an efficient optimization strategy. The experiments on nine synthetics and two real-world cases show the advantages of learned optimization strategies over the state-of-the-art human-designed and meta-learning EA baselines. In addition, due to the tensorization of the operations, DECN is friendly to the acceleration provided by GPUs and runs 102 times faster than EA. © 2023, CC BY.

关键词： optimization algorithms

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Effects of dispatch algorithms and fuel variation on techno-economic performance of hybrid energy system in remote island

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JOURNAL OF ENERGY STORAGE 2024年 78卷

作者： Sakib, Nazmus Hoque, Md Emdadul Rashid, Fazlur Aziz, Muhammad Uddin, Md Shehab Islam, Md. Tariqul Rajshahi Univ Engn & Technol Dept Mech Engn Rajshahi 6204 Bangladesh Missouri Univ Sci & Technol Dept Mech & Aerosp Engn Rolla MO 65401 USA Univ Tokyo Inst Ind Sci 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan

A stand-alone hybrid energy system is needed to mitigate the growing demand for future energy and to reduce emissions generated by conventional fuel sources. This work shows and proposes an optimum hybrid energy system configuration with less environmental pollution for a coastal region of Bangladesh with a load demand of 292.20 kWh/d and a peak load of 41.14 kW. This research presents the cost of energy (COE) and net present cost (NPC) for a PV-wind hybrid system with five alternative fuel generator technologies. For each possible configuration, the effects of three alternative dispatch algorithms, load following (LF), cycle charging (CC), and combined dispatch (CD), are examined. All three algorithms were validated using a genetic algorithm (GA), Cuckoo search algorithm (CUSA), Constrained Particle Swarm optimization (CPSO), Harmony search algorithm (HSA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). Furthermore, a sensitivity analysis is also conducted by utilizing the gasoline price, discount rate, battery cost, PV cost, and inflation rate. The results show that the PV-wind-natural gas-based system provides the minimum COE (0.196 USD/kWh) and NPC (270,483 USD) when the CD algorithm is followed. For the same optimal hybrid energy system, the COE is 0.201 USD/ kWh, 0.0998 USD/kWh, 0.101 USD/kWh, 0.101 USD/kWh, and 0.0987 USD/kWh respectively for GA, CUSA, CPSO, HSA, and NSGA-II. However, COE increases by 8 % (0.212 USD/kWh) and 15 % (0.225 USD/kWh) when the CC and LF algorithms are followed. In addition, a comparison of all configurations reveals that the PV-windbiomass configuration with the CD algorithm excludes biomass generators to lower the COE, making the system emission-free.

关键词： Hybrid energy system Smart grid Renewable energy Cost of energy (COE) Net present cost (NPC) optimization algorithms

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The open banking era: An optimal model for the emergency fund

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 244卷

作者： Liu, Junxiu Huang, Shaodong Fu, Qiang Luo, Yuling Qin, Sheng Cao, Yi Zhai, Jia Yang, Su Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Univ Edinburgh Business Sch Management Sci & Business Econ Grp Edinburgh Scotland Xian Jiaotong Liverpool Univ Business Sch Suzhou Jiangsu Peoples R China Swansea Univ Dept Comp Sci Swansea Wales

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive.

关键词： Emergency fund optimization algorithms Open Banking

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Consensus-based Distributed optimization for Multi-agent Systems over Multiplex Networks

arXiv

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arXiv 2023年

作者： Rodríguez-Camargo, Christian D. Urquijo-Rodríguez, Andrés F. Mojica-Nava, Eduardo A. from Universidad Nacional de Colombia United Kingdom The Corporación Universitaria Minuto de Dios Grupo de Superconductividad y Nanotecnología and Grupo de Óptica e Información Cuántica The Department of Physics Universidad Nacional de Colombia Colombia from Universidad Nacional de Colombia Bogotá Colombia

Multilayer networks provide a more comprehensive framework for exploring real-world and engineering systems than traditional single-layer networks, consisting of multiple interacting networks. However, despite significant research in distributed optimization for single-layer networks, similar progress for multilayer systems is lacking. This paper proposes two algorithms for distributed optimization problems in multiplex networks using the supra-Laplacian matrix and its diffusion dynamics. The algorithms include a distributed saddle-point algorithm and its variation as a distributed gradient descent algorithm. By relating consensus and diffusion dynamics, we obtain the multiplex supra-Laplacian matrix. We extend the distributed gradient descent algorithm for multiplex networks using this matrix and analyze the convergence of both algorithms with several theoretical results. Numerical examples validate our proposed algorithms, and we explore the impact of interlayer diffusion on consensus time. We also present a coordinated dispatch for interdependent infrastructure networks (energy-gas) to demonstrate the application of the proposed framework to real engineering problems. © 2023, CC BY.

关键词： optimization algorithms

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An Augmented Lagrangian for Optimal Control of DAE Systems: Algorithm and Properties

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第1期66卷 261-266页

作者： Aguiar, Marco Aurelio Camponogara, Eduardo Foss, Bjarne Univ Fed Santa Catarina Dept Automat & Syst Engn BR-88040 Florianopolis SC Brazil Norwegian Univ Sci & Technol Engn Cybernet Dept N-7491 Trondheim Norway

This article proposes a relax-and-discretize approach for optimal control of continuous-time differential algebraic systems. It works by relaxing the algebraic equations and penalizing the violation into the objective function using the augmented Lagrangian, which converts the original problem into a sequence of optimal control problems (OCPs) of ordinary differential equations (ODEs). The relax-and-discretize approach brings about flexibility, by allowing the OCPs of ODEs to be solved by the method of choice, such as direct or indirect methods. Conditions are developed for global, local, and suboptimal convergence in terms of the solution of the underlying OCPs. The method is applied to an illustrative example.

关键词： Mathematical model Optimal control Convergence Limiting optimization Prediction algorithms Indexes Differential-algebraic systems nonlinear systems optimal control optimization algorithms

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A Review on optimization of Active Power Filter Placement and Sizing Methods

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ENERGIES 2022年第3期15卷 1175页

作者： Bula, Dawid Grabowski, Dariusz Maciazek, Marcin Silesian Tech Univ Elect Engn & Comp Sci Dept Fac Elect Engn PL-44100 Gliwice Poland Silesian Tech Univ Fac Elect Engn Power Elect Elect Dr & Robot Dept PL-44100 Gliwice Poland

Distortions of current and voltage waveforms from a sinusoidal shape are, not only a source of technical problems, but also have serious economic effects. Their occurrence is related to the common use of loads with nonlinear current-voltage characteristics. These are both high-power loads (most often power electronic switching devices supplying high-power drives), but also widely used low-power loads (power supplies, chargers, energy-saving light sources). The best way to eliminate these distortions is to use active power filters. The cost of these devices is relatively high. Therefore, scientists all over the world are conducting research aimed at developing techniques for the proper placement of these devices, in order to minimize their investment costs. The best solution to this problem is to use optimization techniques. This paper compares the methods and criteria used by the authors of publications dealing with this topic. The summary also indicates a possible direction for further work.

关键词： active power filters harmonics optimization algorithms power quality state of the art review

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A PEMFC model optimization using the enhanced bald eagle algorithm

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AIN SHAMS ENGINEERING JOURNAL 2022年第6期13卷

作者： Alsaidan, Ibrahim Shaheen, Mohamed A. M. Hasanien, Hany M. Alaraj, Muhannad Alnafisah, Abrar S. Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Qassim Saudi Arabia Future Univ Egypt Fac Engn & Technol Elect Engn Dept Cairo 11835 Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Qassim Univ Coll Sci Dept Chem Buraydah 52571 Qassim Saudi Arabia

In this article, a novel optimization algorithm is implemented to estimate the design variables of proton exchange membrane fuel cells. The main objective is to get the minimum of the sum of squared errors (SSE), which is defined as the difference between the measured and the calculated data. The newly devel-oped optimization algorithm is known as the Enhanced Bald Eagle Search (EBES) optimization Algorithm. To verify the proposed algorithm, three tested cases are introduced: BCS 500 W, 250 W, and Horizon H-12 stacks. To study the impact of pressure and temperature variation on the proposed algorithm, the studied cases are repeated under various pressure and temperature conditions. One obstacle to be solved is a nonlinear complex problem that requires an efficient optimization algorithm. A comparison with the existing optimization algorithms is conducted which confirms the effectiveness of the newly developed EBES algorithm. (c) 2022 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 license (http://***/licenses/by/4.0/).

关键词： Bald eagle search Experimental results optimization algorithms PEMFCs parameters estimation

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Revisiting Lq(0 ≤ q < 1) Norm Regularized optimization

arXiv

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arXiv 2023年

作者： Zhou, Shenglong Xiu, Xianchao Wang, Yingnan Peng, Dingtao School of Mathematics and Statistics Beijing Jiaotong University Beijing China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Southwest Forestry University Yunnan China School of Mathematics and Statistics Guizhou University Guizhou China

Sparse optimization has seen its advances in recent decades. For scenarios where the true sparsity is unknown, regularization turns out to be a promising solution. Two popular non-convex regularizations are the so-called L0 norm and Lq norm with q ∈ (0, 1), giving rise to extensive research on their induced optimization. However, the majority of these work centered around the main function that is twice continuously differentiable, and the best convergence rate for an algorithm solving the optimization with q ∈ (0, 1) is superlinear. This paper explores the Lq norm regularized optimization in a unified way for any q ∈ [0, 1), where the main function has a semismooth gradient. In particular, we establish the first-order and the second-order optimality conditions under mild assumptions and then integrate the proximal operator and semismooth Newton method to develop two proximal semismooth Newton pursuit algorithms. Under the second sufficient condition, the whole sequence generated by two algorithms converges to a unique local minimizer. Moreover, the convergence is superlinear and quadratic if the gradient of the main function is semismooth and strongly semismooth at the local minimizer, respectively. Hence, this paper accomplishes the quadratic rate for an algorithm designed to solve the Lq norm regularization problem for any q ∈ (0, 1). Finally, some numerical experiments have showcased their nice performance when compared with several existing solvers. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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RADIAL PARTITIONING WITH SPECTRAL PENALTY PARAMETER SELECTION IN DISTRIBUTED optimization FOR POWER SYSTEMS

arXiv

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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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