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Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第3期46卷 513-523页

作者： Gao, Hongliang Li, Jun Xiong, Lang Zhang, Hongcong Ma, Shuangbao Hubei Normal Univ Sch Elect Engn & Automation Huangshi Peoples R China Wuhan Text Univ Sch Mech Engn & Automation Wuhan 430200 Peoples R China

Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization method combining the moth-flame optimization (MFO) algorithm and fuzzy logic controller (FLC) is proposed to address the problem of poor adaptability of the parameter tuning method in the conventional power system stabilizer (CPSS). This method can optimize the parameters of PSS in different processes. Initially, the optimal parameters of PSS under the current perturbation are given by the MFO algorithm. During the online operation of the system, as perturbation changes, the parameters of the PSS will also be adaptively tuned by the FLC in real-time when the system operating conditions change. According to this method, a fuzzy adaptive proportional-integral-differential (FPID) controller is designed based on the moth-flame optimization algorithm (MFO-FPID), and it is used as PSS to improve dynamic stability performance during oscillation. Moreover, its parameters can be adaptively adjusted in different perturbation scenarios. The designed MFO-FPID controller is applied to the single machine infinite bus (SMIB) power system to compare the dynamic performance with other controllers, that is, proportional-integral-differential (PID) and CPSS. The result shows that the MFO-FPID controller can suppress the oscillation very well, and the control effect is the best.

关键词： Low-frequency oscillation power system stabilizer moth-flame optimization algorithm fuzzy adaptive PID controller single machine infinite bus power system

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An arithmetic and geometric mean-based multi-objective moth-flame optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 6527-6561页

作者： Sahoo, Saroj Kumar Saha, Apu Kumar Houssein, Essam H. Premkumar, M. Reang, Salpa Emam, Marwa M. Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India Minia Univ Fac Comp & Informat Al Minya Egypt Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bangalore 560078 Karnataka India

Expanding the capacity of optimization algorithms for simultaneous optimization of multiple competing objectives is a crucial aspect of research. This study presents MnMOMFO, a novel non-dominated sorting (NDS) and crowding distance (CD)-based multi-objective variant of the moth-flame optimization (MFO) algorithm for multi-objective optimization problems. The algorithm incorporates arithmetic and geometric mean concepts to address MFO's limitations and to improve its performance. Subsequently, we extend this enhanced MFO into a multi-objective variant, leveraging NDS and CD strategies to achieve a well-distributed Pareto optimal front. The effectiveness of the proposed MnMOMFO algorithm is rigorously evaluated across three distinct phases. In the initial phase, we scrutinize its performance on four ZDT multi-objective optimization problems, employing four performance metrics-general distance, inverted general distance, spacing, and spread metric. Comparative analyses with select competitive multi-objective optimization algorithms comprehensively understand MnMOMFO's efficacy. Secondly, 24 complex multi-objective IEEE CEC 2020 test suits are considered on two performance metrics. Namely, Pareto sets proximity and the inverted generational distance in decision space. In the third phase, five real-world engineering problems are considered to measure the problem-solving ability of the MnMOMFO algorithm. The results from the experiments indicated that the MnMOMFO was the best candidate algorithm, achieving more than 95% superior results for multi-objective ZDT benchmark problems, IEEE CEC 2020 test functions, and real-life issues in contrast to several other algorithms. The experimental outcomes substantiate MnMOMFO's superiority, establishing it as a robust and efficient algorithm for multi-objective optimization challenges with broad applicability to real-world engineering problems.

关键词： moth-flame optimization algorithm Multi-objective algorithm Non-dominated sorting Crowding distance IEEE CEC 2020 test functions

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Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-30页

作者： Li, Jing Sun, Shengxiang Xie, Li Zhu, Chen He, Dubo Naval Univ Engn Dept Management Engn & Equipment Econ Wuhan 430033 Peoples R China

In this paper, a novel moth-flame optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://***/loadstar1997/MISMFO.

关键词： moth-flame optimization algorithm Parameter optimization Software effort estimation Support vector regression

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An implementation of inertia control strategy for grid-connected solar system using moth-flame optimization algorithm

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AUTOMATIKA 2024年第1期65卷 206-216页

作者： Nandakumar, N. Symon, V. A. Tibbie Pon Noorul Islam Ctr Higher Educ Dept EEE Kumaracoil India Noorul Islam Ctr Higher Educ Dept EEE Kumaracoil 629180 India

As the solar power system power system grows rapidly, inertia control strategy (ICS) becomes crucial to enable stable grid integration. However, the existing ICS lacks of dynamic weather analysis with maximum power point tracking (MPPT) and fault-ride through (FRT) capabilities such as low voltage ride-through (LVRT) and high voltage ride-through (HVRT). In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization (MFO) algorithm to support vector machine prediction of photovoltaic power generation. In this paper, the proposed adaptive VICS with variable moment of inertia (J) and damping factor $ ({{D_P}} ) $ (DP) demonstrates its effectiveness with faster frequency recovery, less overshooting and continuous stable operation under grid fault and dynamic weather. The MFO algorithm is used to implement inertia control strategies for grid-connected solar systems. Accurate simulation results confirm the inertia control of the emulsion and the control of the solar system. The results of the simulation show a significant improvement in frequency with the designed MFO and compared to Horse Herd optimization (HHO). The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid and maintains the system reliability.

关键词： Inertia control strategy maximum power point tracking moth-flame optimization algorithm solar power system grid-connected PV

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Global and Local moth-flame optimization algorithm for UAV Formation Path Planning Under Multi-constraints

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第3期21卷 1032-1047页

作者： Wu, Xiao-Jing Xu, Lei Zhen, Ran Wu, Xue-Li Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China

To improve the global and local search ability of moth-flame optimization algorithm, three optimization strategies are proposed in this paper, namely chaos-based moth initialization, adaptive weighted position update strategy and population diversity improvement strategy. In moth initialization process, chaos-based Logistic map is adopted to improve population diversity. Then, a nonlinear weighting factor is introduced into the spiral function to adaptively balance the global and local search ability. Besides, new moth is generated by population diversity improvement strategy, which improves diversity and optimality of the population. Finally, simulation tests of unmanned aerial vehicle (UAV) formation under multi-constraints are carried out and comparison results show that the proposed global and local moth-flame optimization algorithm has the superiority in rapidity and optimality in UAV path planning problem compared with the latest path planning algorithms.

关键词： Global search local search moth-flame optimization algorithm path planning unmanned aerial vehicle

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Mathematical formulation and an improved moth-flame optimization algorithm for parallel two-sided disassembly line balancing based on fixed common stations

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第1期10卷 233-249页

作者： Zhang, Yu Zhang, Zeqiang Yin, Tao Liang, Wei Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China

Nowadays, rapid product iterations result in large quantities of end-of-life products. To meet the fast-growing demand for remanufacturing engineering, companies have quickened the standardization and industrialization of waste dissembling. Two-sided disassembly lines can effectively disassemble large-sized products on both sides of the lines, and parallel disassembly lines can disassemble multiple products simultaneously with fewer workstations and higher production efficiency. Combining the two types of disassembly can effectively increase the disassembly efficiency of large-sized products. However, the parallel two-sided disassembly line has not been fully investigated because of the essential complexity of the problem. Therefore, this research introduced the parallel two-sided disassembly line balancing problem with fixed common stations. First, a multi-objective mixed-integer programming model is established to solve the problem for the first time. The model is proved to be correct through small-scale numerical examples. Second, a multi-objective improved moth-flame optimization algorithm is implemented to solve the proposed large-scale problems. The proposed algorithm employs a two-phase decoding approach to design the scheme and a discrete moth for fire operation to search and replace new individuals, and then a restart strategy is introduced to reduce the probability of the population falling into a local optimum. Finally, the algorithm solved extensive disassembly line balancing problems with different layouts, including the straight-line, two-sided, and parallel two-sided, and case studies demonstrated the reliability and validity of the proposed method.

关键词： disassembly line balancing parallel two-sided disassembly line mixed-integer programming model moth-flame optimization algorithm multi-objective optimization

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Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved moth-flame optimization algorithm for Intelligent Route Planning of Multiple UAVs

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IEEE ACCESS 2022年 10卷 49385-49397页

作者： Ma, Mingxi Wu, Jun Shi, Yue Yue, Longfei Yang, Cheng Chen, Xuyi Air Force Engn Univ Air Control & Nav Coll Xian 710051 Peoples R China Air Force Engn Univ Equipment Management & UAV Engn Coll Xian 710051 Peoples R China 93787 Army Beijing 102600 Peoples R China

UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.

关键词： Planning optimization Radar Convergence Missiles Genetic algorithms Autonomous aerial vehicles Route planning moth-flame optimization algorithm cauchy mutation random opposition-based learning chaotic mapping

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The moth-flame optimization algorithm of Abrasive Water Jet Cutting Process 28th

The Moth-Flame Optimization Algorithm of Abrasive Water Jet ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Kawecka, Elzbieta Puzio, Piotr The Jacob of Paradies University Faculty of Technology Teatralna 25 Gorzów Wielkopolski66-400 Poland

The article presents the possibility of using the moth-flame optimization (MFO) algorithm for Abrasive Water Jet machining (AWJ) of structural steel materials. In order to carry out the optimization, an original program was written in Python programming language. In turn, for this optimization process the objective function was determined using the Response Surface Methodology (RSM). Then, a set of control parameters was determined at which the value of the objective function reaches an extreme value. The optimal value calculated based on the moth-flame optimization algorithm was compared with the value of the best effect determined experimentally. © 2024 The Authors.

关键词： Abrasive Water Jet (AWJ) meta-heuristic algorithms moth-flame optimization algorithm optimization algorithms

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optimization Scheduling of Virtual Power Plants with Electric Vehicles Based on moth-flame optimization algorithm

Optimization Scheduling of Virtual Power Plants with Electri...

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2024 China Automation Congress, CAC 2024

作者： Zhou, Jianyu Shi, Jiachuan Zhang, Lianheng Hu, Sining Hao, Liu School of Information and Electrical Engineering Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350368604

In recent years, distributed renewable energy generation has been vigorously developed to address energy crises, environmental pollution, and other issues. However, distributed renewable energy generation exhibits strong randomness and volatility. This paper constructs a dual-layer optimization scheduling model for virtual power plants (VPP) with electric vehicles (EVs), designs operational strategies for VPP integrating EV, and optimizes the output of each component using the moth-flame optimization algorithm. The moth-flame optimization algorithm demonstrates excellent search performance and robustness, suitable for solving complex real-world engineering problems. Simulation results indicate that coordinating output between EVs and controllable power sources can compensate for deviations in renewable energy output, reduce the grid impact caused by standalone EV grid integration, and enhance the supply reliability of virtual power plants. © 2024 IEEE.

关键词： Electric Vehicles moth-flame optimization algorithm Virtual Power Plants

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Address wind farm layout problems by an adaptive moth-flame optimization algorithm

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Yu, Xiaobing Zhang, Wen Nanjing Univ Informat Sci & Technol Res Inst Risk Governance & Emergency Decis Making Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteorol Nanjing 210044 Peoples R China

As a clean renewable energy source, wind energy has been widely used to generate electricity in wind farms. Plenty of turbines work together to form a wind farm to increase electricity output. However, this produces an inevitable wake effect, which affects the efficiency of turbines in capturing wind energy, further leading to a decrease in the power generated by wind farms. To minimize the wake effect, optimizing the turbines layout in wind farms is necessary. Therefore, an adaptive moth-flame optimization algorithm with enhanced Exploration and Exploitation capabilities (MFOEE) is proposed. The refinements of the algorithm include: i) defining a selection probability to utilize diverse information of moths;ii) an enhanced exploitation strategy by pushing moths towards the best flame;iii) developing an enhanced exploration strategy, in which three moths exchange information and two inferior moths fly to the superior moth. To validate the performance of MFOEE, four scenarios are set up using grid-based simulations of actual wind farm conditions. The experimental findings demonstrate that MFOEE can offer the most optimal scheme among the four scenarios, which indicates that MFOEE proves highly effective in addressing wind farm layout optimization challenges.

关键词： moth-flame optimization algorithm Wind energy Wind farm layout optimization Metaheuristic algorithm

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