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A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Zhang, Jian Li, Muxi Yue, Xinxin Wang, Xiaojuan Shi, Maolin Northwestern Polytech Univ Ocean Inst Taicang 215400 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China Anhui Sci & Technol Univ Coll Architecture Bengbu 233030 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.

关键词： hybrid optimization algorithm Differential evolution Teaching-learning-based optimization Surrogate model High -dimensional expensive problems

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A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type

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ENERGY 2013年第1期49卷 119-136页

作者： Narimani, Mohammad Rasoul Azizipanah-Abarghooee, Rasoul Zoghdar-Moghadam-Shahrekohne, Behrouz Gholami, Kayvan Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Kermanshah Branch Kermanshah Iran Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran

This paper presents a new hybrid algorithm based on the Particle Swarm optimization (PSO) and the Shuffle Frog Leaping algorithms (SFLA) for solving the Optimal Power Flow (OFF) in power systems. In consequence of economical issues and increasing of the social welfare, the OPF problem is turning into a pretty remarkable problem and getting more and more important in power systems. The proposed optimization problem has considered the real conditions of power generation involving the prohibit zones, valve point effect and multi-fuel type of generation units. Increasing concerns over the environmental issues forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the OPF problem has become a multi-objective optimization problem. This paper takes advantages of the Pareto optimal solution and fuzzy decision making method in order to achieve the set of optimal solutions and best compromise solution, respectively. The presented algorithm is applied to 30, 57 and 118-bus test systems and the obtained results are compared with those in literature. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow Multi-objective Pareto optimal solution hybrid optimization algorithm Particle swarm optimization Shuffle frog leaping algorithm

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Piecewise temperature dependent electrical equivalent modeling of PEM fuel cell for power conditioning unit design using fuzzy clustering and hybrid optimization

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第1期47卷 6073-6087页

作者： Bankupalli, Phani Teja Ghosh, Subhojit Sahu, Lalit Kumar Dwivedi, Atul Kumar SRM Inst Sci & Technol Elect & Elect Engn Kattankulathur India Natl Inst Technol Elect Engn Dept Raipur Madhya Pradesh India Bundelkhand Inst Engn & Technol Dept ECE Jhansi Uttar Pradesh India

Effective utilization of Polymer Electrolyte Membrane Fuel Cell (PEMFC) for power electronic applications demands a reliable model with accurate parameter estimation. Existing approaches for modeling the PEMFC using terminal characteristics are based on the assumption of uniform temperature throughout the fuel cell operation. However, stack temperature varies widely for different operating conditions. In this regard, the present work proposes a temperature dependent piecewise modeling approach by dividing the operating range into a set of piecewise regions with different model parameter characterizing each region. To achieve the same, the piecewise regions have been distributed using a fuzzy clustering approach by maintaining a compromise between precise representation of experimental data and reduced model complexity. The parameter estimation for each region is carried out using a novel hybrid optimization approach. The model can quantify the dependence of model parameters on temperature variation. With the temperature-dependent piecewise representation, the proposed model significantly outperforms the conventional uniform temperature model in representing the experimental data by achieving a fitting accuracy of 97.9% at 311 K and 99.8% at 319 K, respectively.

关键词： Electrical equivalent model fuel cells fuzzy clustering hybrid optimization algorithm parameter estimation

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A new hybrid optimization ensemble learning approach for carbon price forecasting

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APPLIED MATHEMATICAL MODELLING 2021年 97卷 182-205页

作者： Sun, Shaolong Jin, Feng Li, Hongtao Li, Yongwu Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China

Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Carbon price forecasting Comprehensive evaluation index Model matching strategy hybrid optimization algorithm

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Interval inversion of multiwell logging data for estimating laterally varying petrophysical parameters and formation boundaries

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ACTA GEODAETICA ET GEOPHYSICA 2022年第2期57卷 373-396页

作者： Abdellatif, Mahmoud Szabo, Norbert P. Univ Miskolc Dept Geophys H-3515 Miskolc Hungary South Valley Univ Dept Geol Qena 83523 Egypt Univ Miskolc MTA ME Geoengn Res Grp H-3515 Miskolc Hungary

The present paper introduces a Legendre polynomials based interval inversion method for processing multi-borehole logging data. The method allows the determination of lateral changes of the layer-thicknesses together with the vertical and lateral variations of petrophysical parameters along a 2D cross-section of several boreholes. The method is assessed using noisy synthetic measurements of a petrophysical model made of two-layers structure related to hydrocarbon bearing formations. The numerical experiments aided to investigate the stability and convergence of the 2D interval inversion procedure. To ensure the accuracy and reliability of the inversion results, the misfit of data and model distance are tested, beside the calculation of estimation errors and correlation coefficients. A large amount of input data relative to the number of unknowns results in a high overdetermined ratio, consequently more precise estimates are obtained in stable and convergent procedure than in conventional local (1D) inversion schemes. The feasibility of the 2D interval inversion method is shown by analysing in-situ well logging data acquired in four wells situated in an Egyptian hydrocarbon field.

关键词： Interval inversion Legendre polynomial Global optimization hybrid optimization algorithm Petrophysical parameter Komombo basin Egypt

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A novel dynamic planning mechanism for allocating electric vehicle charging stations considering distributed generation and electronic units

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ENERGY REPORTS 2022年 8卷 14658-14672页

作者： Adetunji, Kayode E. Hofsajer, Ivan W. Abu-Mahfouz, Adnan M. Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn 1 Jan Smuts Ave ZA-2000 Johannesburg South Africa Council Sci & Ind Res CSIR ZA-0184 Pretoria South Africa

Achieving a sustainable and efficient power systems network and decarbonized environment involves the optimal allocation of multiple distributed energy resource (DERs) unit types and flexible alternating current transmission systems (FACTS) to distribution networks. However, while the most focus is on optimization algorithms and multi-objective techniques, little to no attention is paid to the underlying mechanisms in planning frameworks. This paper goes beyond existing literature by investigating the impact of planning mechanisms in smart grid planning frameworks when considering the allocation of PV distributed generation units, battery energy storage systems, capacitor banks, and electric vehicle charging station facilities. First, a single- and multi-objective planning problem is formulated. Then, we propose a novel adaptive-dynamic planning mechanism that uses a recombination technique to find optimal allocation variables of multiple DER and FACTS types. To cope with the additional complexity resulting from the expanded solution space, we develop a hybrid stochastic optimizer, named cooperative spiral genetic algorithm with differential evolution (CoSGADE) optimization scheme, to produce optimal allocation solution variables. Through numerical simulations, it is seen that the proposed adaptive planning mechanism improves achieves a 12% and 14% improvement to the conventional sequential (multi-stage) and simultaneous mechanisms, on small to large scale distribution networks. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by- nc- nd/4.0/).

关键词： Battery energy storage systems Computational intelligence Distributed generation Electric vehicles Electric vehicle charging station hybrid optimization algorithm Planning mechanism Reinforcement learning

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Immune particle swarm optimization of SUV rollover crashworthiness and weight

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2021年第3期64卷 1161-1174页

作者： Jin, Zhilin Zhang, Senhao Weng, Jiansheng Nanjing Univ Aeronaut & Astronaut Dept Vehicle Engn Nanjing 210016 Peoples R China

When designing a sport utility vehicle (SUV), designers strive to improve the vehicle's rollover crashworthiness while avoiding a significant increase in its weight. To aid in optimizing such a trade-off, this paper proposes a multi-disciplinary and multi-objective hybrid optimization algorithm that combines particle swarm optimization and the artificial immune method. First, the SUV structure's influence on body mass and rollover crashworthiness is studied using contribution analysis, and structural improvements are discussed according to Federal Motor Vehicle Safety Standard 216. Building on the analysis results, the SUV's rollover crashworthiness and weight optimization model are proposed. Radial basis function neural network and a genetic algorithm are used to build and optimize surrogate models of total weight, maximum contact force, and torsion frequency. The proposed algorithm then utilizes particle swarm and artificial immune to seek Pareto solutions that optimize SUV structure. Finally, the technique for order preference by similarity to ideal solution method determines a final solution from Pareto-optimal solutions. Compared to previous studies, the results show that the proposed hybrid optimization algorithm improves the Pareto solution sets' diversity and distribution uniformity, enhances SUV rollover crashworthiness, and reduces SUV structure components' weight.

关键词： SUV rollover crashworthiness Weight reduction Material structure integrated design Multi-disciplinary and multi-objective optimization hybrid optimization algorithm

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A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷 e6303-e6303页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Kumari, Archana Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Jharkhand India

optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in-depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self-clearing fault conditions. A multi-objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO-CS and BMO-PSO), and harris hawks optimizer.

关键词： barnacles matting optimization cuckoo search Harris hawks optimizer hybrid optimization algorithm power system oscillation damping

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Adaptive protection coordination in microgrid based on nature inspired meta-heuristic optimization algorithm

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第2期6卷

作者： Kumari, Rani Naick, Bhukya Krishna Indian Inst Technol IIT Dept Elect Engn Dhanbad India

Ensuring a robust protection system is crucial for safeguarding the integrity of the overall system against abnormalities. Incorporating distributed generation (DG) into the distribution network can introduce fluctuations in fault current levels and directions, potentially causing mismatches in the response of the existing coordination system. This study proposes an adaptive protection coordination scheme designed to accommodate both grid-connected and standalone modes, addressing various fault scenarios. Utilizing a hybrid WCMFO algorithm, optimal relay settings are determined to facilitate effective coordination within a microgrid setup. The proposed method has been analyzed on 9 bus Canadian benchmark system integrated with four DGs. The performance of the proposed method is compared to other optimization techniques to demonstrate its effectiveness. System modelling is conducted using MATLAB/Simulink, and validation is further carried out using industrial ETAP software on a test microgrid system. The analysis extends to evaluating the enhancement in overall system reliability, quantified in terms of energy not supplied (ENS).

关键词： adaptive protection distributed generation DOCR hybrid optimization algorithm protection coordination

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hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

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SCIENTIFIC REPORTS 2024年第1期14卷 1-29页

作者： Mohammad, Saleem Jeebaseelan, S. D. Sundarsingh Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 India

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

关键词： hybrid optimization algorithm Sine-Cosine algorithm (SCA) Spotted Hyena Optimizer (SHO) Chimp optimization algorithm (ChOA) Proportional-integral (PI) controllers Microgrid Renewable energy integration Integral of Time-Weighted Squared Error (ITSE)

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