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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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Fast bearing fault diagnosis of rolling element using Levy moth-flame optimization algorithm and Naive Bayes

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2020年第4期22卷 730-740页

作者： Sun, Shuang Przystupa, Krzysztof Wei, Ming Yu, Han Ye, Zhiwei Kochan, Orest Hubei Univ Technol Sch Comp Sci Wuhan 430068 Hubei Peoples R China Lublin Univ Technol Ul Nadbystrzycka 36 PL-20618 Lublin Poland Wuhan FiberHome Telecommun Technol Co Ltd Wuhan FiberHome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China Lviv Polytech Natl Univ Karpinskoho Str 1 Lvov Ukraine

Fault diagnosis is part of the maintenance system, which can reduce maintenance costs, increase productivity, and ensure the reliability of the machine system. In the fault diagnosis system, the analysis and extraction of fault signal characteristics are very important, which directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis method based on the ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Levy flight (LMFO) and the naive Bayes (NB) is proposed, which combines traditional pattern recognition methods meta-heuristic search can overcome the difficulty of selecting classifier parameters while solving small sample classification under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and methods was also displayed in detail. The results manifest the efficiency and accuracy of signal sparse representation and fault type classification has been enhanced.

关键词： malfunction diagnostics naive Bayes moth-flame optimization algorithm ensemble empirical mode decomposition

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A covariance-based moth-flame optimization algorithm with Cauchy mutation for solving numerical optimization problems

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APPLIED SOFT COMPUTING 2022年第0期119卷 108538-108538页

作者： Zhao, Xiaodong Fang, Yiming Liu, Le Xu, Miao Li, Qiang Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao 066004 Peoples R China Hebei Normal Univ Coll Engn Shijiazhuang 050024 Peoples R China

moth-flame optimization (MFO) algorithm, which is inspired by the navigation method of moths, is a nature-inspired optimization algorithm. The MFO is easy to implement and has been used to solve many real-world optimization problems. However, the MFO cannot balance exploration and exploitation well, and the information exchange between individuals is limited, especially in solving some complex numerical problems. To overcome these disadvantages of the MFO in solving the numerical optimization problems, a covariance-based moth-flame optimization algorithm with Cauchy mutation (CCMFO) is proposed in this paper. In the CCMFO, the concept of covariance is used to transform the individuals of the moths and flames from the original space to the eigenspace and update the positions of moths, which can better improve the information exchange ability of the flames and moths in the eigenspace. In addition, Cauchy mutation is utilized to improve the exploration. And the CCMFO is compared with the other 22 algorithms on CEC 2020 test suite. The test results show that the CCMFO is better than other population-based optimization algorithms and MFO variants in search performance, while its performance is statistically similar to CEC competition algorithms. Furthermore, the CCMFO is compared with the other 12 algorithms on CEC 2020 real -world constrained optimization problems, and the results show that the CCMFO can effectively solve real-world constrained optimization problems. Finally, the CCMFO is used to optimize the tracking controller parameters of continuous casting mold vibration displacement. The experimental results based on the experimental platform show that the CCMFO can effectively reduce the difficulty of parameter selection and improve the tracking accuracy. (c) 2022 Elsevier B.V. All rights reserved.

关键词： moth-flame optimization algorithm Covariance matrix Cauchy mutation Numerical optimization problems Engineering optimization problems

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Multi-swarm improved moth-flame optimization algorithm with chaotic grouping and Gaussian mutation for solving engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 204卷

作者： Zhao, Xiaodong Fang, Yiming Ma, Shuidong Liu, Zhendong Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China North China Univ Sci & Technol Coll Continuing Educ Tangshan 063210 Peoples R China

moth-flame optimization (MFO) is widely utilized to solve optimization problems in different fields since it has a simple structure and easy implementation. However, MFO cannot effectively balance exploration and exploitation and often suffers from the lack of population diversity in the search process, especially in solving some complex engineering optimization problems. To overcome the above problems, in this paper, a multiswarm improved moth-flame algorithm (MIMFO) is proposed. In MIMFO, firstly, the population is grouped and dynamically reorganized through chaotic grouping mechanism and dynamic regrouping mechanism, which can improve the grouping quality and diversity of the population. Secondly, spiral search and linear search are carried out for the two sub-swarms to improve the search efficiency and balance exploration and exploitation. In addition, Gaussian mutation is used to generate flame, which can accelerate convergence and enhance the exploration. The MIMFO is verified on 13 benchmark problems with 30, 500, 1000, 2000 dimensions and CEC 2014 test functions. The results show that the MIMFO is superior to other swarm intelligence algorithms and MFO variants in finding the global optimum and convergence performance. Finally, MIMFO is used to solve 57 engineering constraint optimization problems, and the results show that MIMFO can solve real-world engineering problems.

关键词： moth-flame optimization algorithm Multi-swarm Gaussian mutation Linear search Engineering optimization problems

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

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Procedia Computer Science 2024年 246卷 2912-2921页

作者： Elżbieta Kawecka Piotr Puzio The Jacob of Paradies University Faculty of Technology Teatralna 25 66-400 Gorzów Wielkopolski 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.

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

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DMFO-CD: A Discrete moth-flame optimization algorithm for Community Detection

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algorithmS 2021年第11期14卷 314页

作者： Nadimi-Shahraki, Mohammad H. Moeini, Ebrahim Taghian, Shokooh Mirjalili, Seyedali Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad *** Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad *** Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

In this paper, a discrete moth-flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth-flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.

关键词： community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth-flame optimization algorithm

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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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An improved moth-flame optimization algorithm with periodic mutation and Gaussian mutation for safety-enhanced UAV path planning

An improved moth-flame optimization algorithm with periodic ...

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第43届中国控制会议

作者： Xiaodong Zhao Zhiqiang Hu Xiaoqian Li Xianliang Zhang School of Mathematics and Statistics Taishan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To handle the path planning problem of unmanned aerial vehicles(UAV) meeting numerous obstacles in complicated environments, an improved moth-flame optimization(MFO) algorithm with periodic and Gaussian mutations(PGMFO) is proposed. For PGMFO, firstly, moths are able to jump out of local optima and better their position by means of periodic ***, the Gaussian mutation is used to select the optimal flame in each iteration can improve the development capability of MFO. Finally, PGMFO is contrasted with other 9 optimization algorithms using 23 test problems. According to the findings,PGMFO performs faster and more accurately during convergence in the majority of optimization issues. In addition, in the generation of benchmark scenes in real digital elevation model maps, PGMFO is used to solve the optimization problem of feasible and safe operation requirements and constraints for UAVs. The results show that the validity of the PGMFO.

关键词： moth-flame optimization algorithm periodic mutation Gaussian mutation UAV path planning

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A New Bionic Swarm Intelligence optimization:Construction and Application of Modified moth-flame optimization algorithm

A New Bionic Swarm Intelligence Optimization:Construction an...

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2017 International Conference on Mechanical and Mechatronics Engineering(ICMME2017)

作者： Ge-ning XU Shuang-yun HUANG Qi-song QI Zhong-yuan KANG College of Mechanical Engineering Taiyuan University of Science and Technology

The moth-flame optimization algorithm is a new bionic swarm intelligence *** the moth's behavior has a large number of random states and need to repeatedly test in the algorithm,which takes *** this paper,the basic principle of the moth-flame algorithm is analyzed deeply,and proposed a modified moth-flame *** core is to improve and optimize the adaptive mechanism for the number of flames,and to change the flame adaptive mechanism along the straight line to decrease along the curve,so as to improve the convergence speed of the adaptive flame number;Given the ability of "study" to the moths when moths update position,that all moths update the position with reference to the best flame,so as to improve the search *** testing 8 classical test functions and 1 engineering example,it is proved that the modified moth-flame algorithm has the advantages of faster convergence speed,higher search precision and avoiding local *** significant computational efficiency and precision of the improved moth-flame algorithm can be used to improve the ability to solve practical engineering problems.

关键词： moth-flame optimization algorithm Modified Adaptive mechanism Study

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