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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-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2017年第1期34卷 42-51页

作者： Khalilpourazari, Soheyl Pasandideh, Seyed Hamid Reza Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran

In this paper, a multi-item multi-constrained Economic Order Quantity model with nonlinear unit holding cost and partial backordering is proposed. To develop an applicable model, different technical, physical, and strategic constraints are considered such as available budget, warehouse capacity, total permissible holding cost, and total permissible backordering cost constraints. The goal is to determine the lengths of inventory cycles, where the inventory level is positive and negative such that the total inventory costs are minimized. In addition, backordering rate during shortage period for each product is considered as a decision variable which can significantly reduce the total inventory costs. Due to complexity and nonlinearity of the proposed model, interior-point method and moth-flame optimization algorithm are utilized to solve the model in different sizes. At the end, the performance of the solution methods is compared statistically considering three measures to determine the superior solution method.

关键词： Economic order quantity moth-flame optimization algorithm partial backordering nonlinear unit holding cost interior-point method constrained nonlinear optimization

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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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Optimal Placement and Sizing of Distributed Generation Units for Power Loss Reduction Using moth-flame optimization algorithm

Optimal Placement and Sizing of Distributed Generation Units...

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Das, Anurag Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

ISBN: (纸本)9781509061068

In this paper, a heuristic nature inspired moth-flame optimization algorithm is used for determining an optimal location and size of Distributed Generation units (DG) in radial distribution system. The benefits of a DG can be utilized only when the location and capacity of DG units are optimal. In this paper recently developed moth-flame optimization algorithm is employed which uses special navigation methods of moths in presence of moonlight (flame) to travel long distance in a straight line. This behavior of moths is used to optimize the location and size of DG. The prime objective of this paper is to minimize the real power loss. The proposed technique has been tested on a 33 bus radial distribution system. A factor called Index vector is used to screen the most sensitive buses for DG placement. Then these screened buses are taken as search space in the optimization algorithm which considerably reduces the computational time.

关键词： moth-flame optimization algorithm Distributed Generation(DG) Radial distribution system Index vector

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An Ameliorated moth-flame optimization algorithm 37

An Ameliorated Moth-flame Optimization Algorithm

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37th Chinese Control Conference (CCC)

作者： Zhao, Xiao-dong Fang, Yi-ming Ma, Zhuang Xu, Miao Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Hebei Peoples R China Tangshan Univ Tangshan 063000 Hebei Peoples R China

ISBN: (纸本)9789881563958

To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimization algorithms. The moth-flame optimization (MFO) algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO, an ameliorated moth-flame optimization algorithm (A-MFO) that combines the crisscross optimization algorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.

关键词： moth-flame optimization algorithm crisscross optimization algorithm stochastic optimization convergence

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An improved moth-flame optimization algorithm based on fusion mechanism 47

An improved moth-flame optimization algorithm based on fusio...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Jiang, Luchao Hao, Kuangrong Tang, Xue-song Wang, Tong Liu, Xiaoyan Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781665435543

moth-flame optimization algorithms are widely employed to solve optimization problems and achieve good performance. However, the algorithms suffer the shortcoming of prematurity because of the early gathering of flames. To solve this problem, the flame fusion mechanism is integrated to improve the exploratory behavior of the moth-flame optimization algorithm. The flame fusion mechanism provides a new way to evaluate the state of flame aggregation based on the distribution of flames and moths. When the concentration of flames is higher than the fusion threshold, the better flame will fuse other flames. And the fused flames will be regenerated to enhance the exploration behavior of the algorithm. At the same time, the fusion rate that determines the probability of flame fusion is introduced. The fusion rate changes during iteration to balance the exploration and exploitation behaviors of the algorithm. The improved moth-flame optimization is validated by ten benchmark functions. The results show that the optimization ability of the improved moth-flame optimization algorithm is improved, and the stability is higher than compared algorithms as well.

关键词： moth-flame optimization algorithm optimization problem swarm intelligence fusion mechanism

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Optimal Integration of Renewable Distributed Generation in Practical Distribution Grids based on moth-flame optimization algorithm

Optimal Integration of Renewable Distributed Generation in P...

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International Conference on Advanced Electrical Engineering (ICAEE)

作者： Settoul, Samir Zellagui, Mohamed Abdelaziz, Almoataz Youssef Chenni, Rachid Mentouri Univ Constantine 1 Dept Electrotech Constantine Algeria Ecole Technol Super Dept Elect Engn Montreal PQ Canada Future Univ Egypt Fac Engn & Technol Cairo Egypt

ISBN: (纸本)9781728122205

Distributed generator (DG) resources are small scale electric power generating plants that can provide power in distribution grids. The above benefits can be achieved by optimal integration of DG using novel optimization algorithm namely moth-flame optimization (MFO) algorithm for determine the optimal location and sizing to reduce the power losses and augmented voltage stability index. The proposed algorithm is evaluated on IEEE 69-bus, and practical radial distribution grids: Constantine City 73-bus and Indian 85-bus. The installed DGs are photovoltaic (PV) and wind turbine (WT) sources. A numerical simulation including comparative studies was presented to demonstrate the performance and applicability of the MFO algorithm. The validity of the proposed MFO algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques.

关键词： renewable distributed generation optimal integration distribution grids power loss voltage stability index moth-flame optimization algorithm

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Feature Selection of Parallel Binary moth-flame optimization algorithm Based on Spark 3

Feature Selection of Parallel Binary Moth-flame Optimization...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Chen, Hongwei Fu, Heng Cao, Qianqian Han, Lin Yan, Lingyu Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538662434

In view of the good classification ability of moth-flame optimization (MFO) in reducing feature redundancy, this paper applied MFO algorithm to feature selection. However, the MFO algorithm is easy to fall into local optimum and has a weak search ability, which severely limits the classification performance and dimensional reduction ability of the algorithm. Therefore, this paper combined MFO algorithm with distributed parallel computing Spark platform distributed,and proposed a feature selection method based on Spark Parallel Binary moth-flame optimization (SPBMFO) algorithm. The experimental results show that compared with the classical particle swarm optimization algorithm(PSO), the genetic algorithm(GA) and the cuckoo search algorithm(CS), when using the binary MFO algorithm for feature selection, the selected features are improved by 12.5%, 15% and 2.5%, respectively. SPBMFO algorithm avoids the search process falling into local optimum and improve the classification performance of the algorithm, which minimizes the number of features while maximizing the classification performance.

关键词： moth-flame optimization algorithm Parallel computing Feature Selection Spark Framework

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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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