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Efficient Model Points Selection in Insurance by Parallel Global optimization Using Multi CPU and Multi GPU

BUSINESS & INFORMATION SYSTEMS ENGINEERING 2020年第1期62卷 5-20页

作者： Ferreiro-Ferreiro, Ana Maria Garcia-Rodriguez, Jose Antonio Souto, Luis A. Vazquez, Carlos Univ A Coruna Fac Informat Dept Math La Coruna 15071 Spain Univ A Coruna Fac Informat CITIC La Coruna 15071 Spain

In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, ). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282-298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting.

关键词： Model points portfolio Risk functional hybrid optimization algorithms Differential evolution Basin hopping Monte Carlo simulation HPC Multi CPU Multi GPU

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Multi-objectives transmission expansion planning considering energy storage systems and high penetration of renewables and electric vehicles under uncertain conditions

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ENERGY REPORTS 2024年 11卷 4143-4164页

作者： Al-Dhaifallah, Mujahed Refaat, Mohamed M. Alaas, Zuhair Aleem, Shady H. E. Abdel El-kholy, Elwy E. Ali, Ziad M. King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr lRC Renewable Energy & P KFUPM Box 120 Dhahran 31261 Saudi Arabia Elect Res Inst Photovolta Cells Dept Cairo 11843 Egypt Jazan Univ Coll Engn Dept Elect Engn Jizan 45142 Saudi Arabia Inst Aviat Engn & Technol Dept Elect Engn Giza 25152 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Elect Engn Dept Wadi Aldawasir 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt

Transmission expansion planning (TEP) integrating electric vehicles (EVs) and renewable energy sources (RESs) is pivotal for the transition toward cleaner and more sustainable energy systems. One of the biggest challenges in TEP with EVs and RESs is the uncertainty inherent in their behaviors. Managing this uncertainty is critical for ensuring the grid's reliability and resilience, and for facilitating a seamless transition to EVs. A potent method for addressing uncertainties in power systems is the application of a scenario-based approach. However, the efficiency of this approach is contingent upon an increase in the number of representative scenarios, which, in turn, escalates computational time, making solving the TEP problem a crucial task. To tackle these challenges, this paper proposes a multi-objective resilience model for TEP, to advance towards more sustainable energy systems. The model seeks to strike a balance between economic, environmental, and technical considerations by minimizing planning costs, carbon dioxide emissions, and enhancing the voltage profile. Furthermore, the paper proposes a cascaded intelligent strategy to handle uncertainties and reduce computational complexity by reducing the number of representative scenarios without compromising system reliability. The adaptive neuroinference system is designed to manage long-term uncertainties, while two fuzzy systems are developed to address short-term uncertainties. The problem is formulated as a complex non-linear multi-objective optimization problem. To address this, a hybrid approach combining the mountain gazelle optimizer and the multiobjective salp swarm optimizer is developed to solve the problem. The efficacy of the proposed method is validated on two separate test systems, demonstrating its superiority in maintaining system reliability while reducing computing time by at least 92 % in comparison with the considered traditional methods. The results also highlight the superior performance

关键词： Transmission expansion planning Electric vehicles Renewable energy sources Fuzzy systems hybrid optimization algorithms

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Topological Active Volume 3D segmentation model optimized with genetic approaches

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NATURAL COMPUTING 2012年第1期11卷 161-174页

作者： Novo, Jorge Barreira, Noelia Gonzalez Penedo, Manuel Santos, Jose Univ A Coruna Dept Comp Sci Coruna 15071 Spain

The Topological Active Volumes is an active model focused on 3D segmentation tasks. It is based on the 2D Topological Active Nets model and provides information about the surfaces and the inside of the detected objects in the scene. This paper proposes new optimization approaches based on Genetic algorithms that improve the results of the 3D segmentations and overcome some drawbacks of the model related to parameter tuning or noise conditions. The hybridization of the genetic algorithm with a greedy local search allows the treatment of topological changes in the model, with the possibility of an automatic subdivision of the Topological Active Volume. This combination integrates the advantages of the global and local search procedures in the segmentation process.

关键词： Genetic algorithms Image segmentation hybrid optimization algorithms Lamarck strategy Topological Active Volumes

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Globally-biased BIRECT algorithm with local accelerators for expensive global optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2020年第0期144卷 113052-000页

作者： Paulavicius, Remigijus Sergeyev, Yaroslav D. Kvasov, Dmitri E. Zilinskas, Julius Vilnius Univ Inst Data Sci & Digital Technol Akad 4 LT-08663 Vilnius Lithuania Univ Della Calabria Dipartimento Ingn Informat Modellist Elettron & S Via P Bucci41C I-87036 Arcavacata Di Rende CS Italy Lobachevsky State Univ Dept Software & Supercomp Technol Nizhnii Novgorod Russia

In this paper, black-box global optimization problem with expensive function evaluations is considered. This problem is challenging for numerical methods due to the practical limits on computational budget often required by intelligent systems. For its efficient solution, a new DIRECT-type hybrid technique is proposed. The new algorithm incorporates a novel sampling on diagonals and bisection strategy (instead of a trisection which is commonly used in the existing DIRECT-type algorithms), embedded into the globally-biased framework, and enriched with three different local minimization strategies. The numerical results on a test set of almost 900 problems from the literature and on a real-life application regarding nonlinear regression show that the new approach effectively addresses well-known DIRECT weaknesses, has beneficial effects on the overall performance, and on average, gives significantly better results compared to several DIRECT-type methods widely used in decision-making expert systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Nonlinear global optimization DIRECT-Type algorithms BIRECT Algorithm hybrid optimization algorithms Nonlinear regression

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Fusion of hard and soft control strategies for the robotic hand /

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2017年

作者： Chen Cheng-Hung

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An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function

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ENERGY 2023年 264卷

作者： Calasan, Martin Aleem, Shady H. E. Abdel Hasanien, Hany M. Alaas, Zuhair M. Ali, Ziad M. Univ Montenegro Fac Elect Engn Podgorica Montenegro Sci Valley Acad Valley High Inst Engn & Technol Dept Elect Engn Qalyubia 44971 Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Jazan Univ Coll Engn Elect Engn Dept Jazan 45142 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Elect Engn Dept Wadi Addawaser 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt

In the literature, identifying the parameters of proton exchange membrane fuel cells (PEMFCs) relies on minimizing the difference between measured and simulated voltage values for different current values. In this regard, expressions representing the PEMFC voltage as a current function are usually used. Unlike literature approaches, in this work, a new mathematical expression of PEMFC current as a voltage function is derived and solved using the iterative Lambert W function for the first time in literature. The root-mean-square error (RMSE) and sum-ofsquares error (SSE) between the measured and estimated current and voltage values are calculated, analyzed, and discussed using the parameters obtained from many optimization methods for two PEMFCs - Ballard-Mark-V 5 kW and BCS 500 W. An improved evaporation rate water cycle algorithm (ERWCA), named Chaotic ERWCA (ChERWCA), is employed to solve the new PEMFC parameter estimation problem based on minimizing SSE between the measured and estimated current values obtained using the new mathematical model. Moreover, SSE and RMSE of voltage and current measures were calculated for different recent methods presented in the literature. The results validate the effectiveness of the new mathematical model and show that the solutions obtained using ChERWCA outperformed those obtained by other methods presented for these fuel cells in the literature. The improvement of voltage SSE using the new mathematical formulation of the PEMFC model and the proposed algorithm is about 0.1% compared to the best result reported in the literature. Finally, this work lays the foundation for developing a novel mathematical model to investigate the operation of PEMFCs in modern power systems.

关键词： Energy storage Lambert W function hybrid optimization algorithms Proton exchange membrane fuel cells Special trans function theory

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Optimal Coordination of Directional Overcurrent Relays Using hybrid Firefly-Genetic Algorithm

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ENERGIES 2023年第14期16卷 5328页

作者： Foqha, Tareq Khammash, Maher Alsadi, Samer Omari, Osama Refaat, Shady S. Al-Qawasmi, Khaled Elrashidi, Ali Arab Amer Univ Fac Engn & Informat Technol Dept Elect Engn POB 240 Jenin Palestine Annajah Natl Univ Dept Elect Engn POB 7 Nablus Palestine Palestine Tech Univ Kadoorie Dept Elect Engn P305 Tulkarm Palestine Texas A&M Univ Qatar Elect & Comp Engn Dept POB 23874 Doha Qatar Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield AL10 9EU England Zarqa Univ Cybersecur Dept Zarqa 13133 Jordan Univ Business & Technol Coll Engn Elect Engn Dept Jeddah 21589 Saudi Arabia

The application of directional overcurrent relays (DOCRs) plays an important role in protecting power systems and ensuring their safe, reliable, and efficient operation. However, coordinating DOCRs involves solving a highly constrained and nonlinear optimization problem. The primary objective of optimization is to minimize the total operating time of DOCRs by determining the optimal values for decision variables such as the time multiplier setting (TMS) and plug setting (PS). This article presents an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to achieve improved solutions. First, this study modifies the firefly algorithm to obtain a global solution by updating the firefly's brightness and to prevent the distance between the individual fireflies from being too far. Additionally, the randomized movements are controlled to produce a high convergence rate. Second, the optimization problem is solved using the genetic algorithm. Finally, the solution obtained from the modified firefly algorithm is used as the initial population for the genetic algorithm. The proposed algorithms have been tested on the IEEE 3-bus, 8-bus, 9-bus and 15-bus networks. The results indicate the effectiveness and superiority of the proposed algorithms in minimizing the total operating time of DOCRs compared with other optimization methods presented in the literature.

关键词： directional overcurrent relay coordination genetic algorithm firefly algorithm hybrid optimization algorithms power system protection

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A novel hybrid self-adaptive heuristic algorithm to handle single- and multi-objective optimal power flow problems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106492-106492页

作者： Naderi, Ehsan Pourakbari-Kasmaei, Mahdi Cerna, Fernando, V Lehtonen, Matti Southern Illinois Univ Sch Elect Comp & Biomed Engn Carbondale IL USA Alto Univ Dept Elect Engn & Automat Espoo 02150 Finland Univ Fed Roraima Dept Elect Engn Av Cap Ene Garces 2413 BR-69310000 Boa Vista Parana Brazil

The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems.

关键词： Emission control Fuzzy adaptive feature hybrid optimization algorithms Self-adaptive particle swarm optimization Differential evolution Optimal power flow Pareto optimal method Practical constraints

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Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

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ENERGY 2024年 308卷

作者： Hasanien, Hany M. Alsaleh, Ibrahim Alassaf, Abdullah Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Univ Hail Coll Engn Dept Elect Engn Hail 55211 Saudi Arabia

In this article, a novel optimization algorithm named hybrid Osprey optimization and Particle Swarm optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey optimization Algorithm (OOA), Particle Swarm optimization (PSO), and Heap optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.

关键词： Electric-vehicle integration hybrid optimization algorithms Probabilistic optimal power flow Wave energy integration

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