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第37届中国控制会议

作者： Xiao-dong Zhao Yi-ming Fang Zhuang Ma Miao Xu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Tangshan University

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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optimization Improvement and Clustering Application Based on moth-flame algorithm 18th

Optimization Improvement and Clustering Application Based on...

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18th International Conference on Intelligent Computing (ICIC)

作者： Ye, Lvyang Huang, Huajuan Wei, Xiuxi Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Peoples R China Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

optimization problem is widely used in production management, military command and scientific experiments and other fields, moth-flame optimization algorithm as a new swarm intelligence optimization algorithm, has the advantages of fast convergence, simple structure, strong robustness, memory mechanism, it is also one of the focus of scholars. K-means clustering is the most famous partition clustering algorithm. Given a set of data points and the required number of clustering k, k is specified by the user, and the K-means algorithm repeatedly divides the data into K clusters according to a certain distance function. This article first on moth-flame optimization algorithm (MFO) the existence of complex or slightly larger scale function to solve the problems of slow convergence speed, put forward by the flame number greater than the number of moths reference grey wolf optimizer (GWO) comes first algorithm do rectilinear flight, later periods the scaling factor are introduced to improve moth-flame optimization algorithm, in order to realize broaden the moths search area, improve the ability of global optimization and convergence rate of the target. Through experiments to verify the feasibility of the improved moth-flame optimization algorithm (IMFO), the convergence speed is significantly higher than MFO algorithm, and the solution accuracy is also greatly improved. Then the algorithm is used to guide the clustering center of k-means clustering algorithm to improve the clustering accuracy. The three algorithms of K-means, MFO Fusion K-means (MFO-KM) and IMFO Fusion K-means (IMFO-KM) algorithms were compared in the international standard data set Iris, Seeds and Wine Quality. The results showed that: IMFO-KM algorithm has the best performance improvement in Wine Quality data set, with the accuracy improved by 3.82%-6.37% compared with other algorithms, the class average distance G reduced by 7.18%-13.58%, and the standardized mutual information improved by 14.17%.

关键词： moth-flame optimization algorithm Clustering center K-means clustering

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Inference on diffusion processes related to a general growth model

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STATISTICS AND COMPUTING 2025年第2期35卷 1-19页

作者： Albano, Giuseppina Barrera, Antonio Giorno, Virginia Torres-Ruiz, Francisco Univ Salerno Dept Polit & Social Studies Via Giovanni Paolo II I-84084 Fisciano SA Italy Univ Malaga Dept Math Anal Stat & Operat Res & Appl Math Bulevar Louis Pasteur 31 Malaga 29010 Spain Univ Salerno Dept Comp Sci Via Giovanni Paolo II I-84084 Fisciano SA Italy Univ Granada Dept Stat & Operat Res Avda Fuentenueva S-N Granada 18071 Spain Univ Granada Inst Math IMAG C Ventanilla 11 Granada 18001 Spain

This paper considers two stochastic diffusion processes associated with a general growth curve that includes a wide family of growth phenomena. The resulting processes are lognormal and Gaussian, and for them inference is addressed by means of the maximum likelihood method. The complexity of the resulting system of equations requires the use of metaheuristic techniques. The limitation of the parameter space, typically required by all metaheuristic techniques, is also provided by means of a suitable strategy. Several simulation studies are performed to evaluate to goodness of the proposed methodology, and an application to real data is described.

关键词： Lognormal process Gaussian process Estimation moth-flame optimization algorithm

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An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks

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EXPERT SYSTEMS WITH APPLICATIONS 2019年第0期129卷 135-155页

作者： Xu, Yueting Chen, Huiling Heidari, Ali Asghar Luo, Jie Zhang, Qian Zhao, Xuehua Li, Chengye Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Wenzhou Med Univ Dept Pulm & Crit Care Med Affiliated Hosp 1 Wenzhou 325000 Peoples R China

moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on the navigation routine of moths in the environment known as transverse orientation. For some complex optimization tasks, especially high dimensional and multimodal problems, the conventional MFO may face problems in the convergence trends or be trapped into the local and deceptive optima. Therefore, in this study, two strategies have been introduced into the conventional MFO to get a more stable sense of balance between the exploration and exploitation propensities. First, Gaussian mutation is employed to increase the population diversity of MFO. Then, a chaotic local search is applied to the flame updating process of MFO for better exploiting the locality of the solutions. The proposed CLSGMFO approach was compared against a wide range of well-known classical metaheuristic algorithms (MAs) and various advanced MAs using 23 classical benchmark functions. It was shown that the designed CLSGMFO can outperform most of the popular MAs in terms of solution quality and convergence speed. Moreover, based on CLSGMFO, a hybrid kernel extreme learning machine model, which is called CLSGMFO-KELM, is established to deal with financial stress prediction scenarios. To investigate the effectiveness of the CLSGMFO-KELM model, the proposed hybrid system was tested on two widely used financial datasets and compared against a broad array of popular classifiers. The results demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance. Accordingly, the proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： moth-flame optimization algorithm Parameter optimization Chaotic local search Gaussian mutation Kernel extreme learning machine

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Dynamic performance enhancement for wind energy conversion system using moth-flame optimization based blade pitch controller

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2018年第Jun.期27卷 206-212页

作者： Ebrahim, M. A. Becherif, M. Abdelaziz, Almoataz Y. Benha Univ Fac Engn Shoubra Dept Elect Engn 108 Shoubra StPOB 11241 Cairo Egypt Univ Technol Belfort FEMTO ST UMR CNRS 6174 FC Lab FR CNRS 3539 Dept Energy Montbeliard France Ain Shams Univ Fac Engn Dept Elect Engn Cairo Egypt

moth-flame optimization (MFO) technique has recently been explored to develop a novel algorithm for distributed optimization and control. In this paper, the MFO-based design of blade pitch controllers (BPCs) is proposed for wind energy conversion system (WECS) to enhance the damping of oscillations in the output power and voltage. The simple Proportional-Integral-Differential (PID) is used to realize the advantage of the proposed hybrid referential integrity MFO technique. The proposed blade pitch controllers are termed as BPC-PID (MFO). Single wind turbine system, equipped with BPC-PID (MFO), is considered to accomplish this study. The suggested WECS model considers small as well as large scale uncertainties. MFO is utilized to search for optimal controller parameters by minimizing a candidate time-domain based objective function. The performance of the proposed controller has been compared to those of the conventional PID controller based on Zeigler Nichols and simplex algorithm and the PID controller optimized by genetic algorithms (GA), to demonstrate the superior efficiency of the MFO-based BPC-PID. Simulation results emphasis on the better performance of the proposed BPC-PID (MFO) compared to conventional and GA-based BPC-PID controllers over a wide range of operating conditions and control system parameters uncertainties.

关键词： Blade pitch control Dynamic performance moth-flame optimization algorithm Robustness Wind energy conversion system

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Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 63卷 20-32页

作者： Savsani, Vimal Tawhid, Mohamed A. Pandit Deendayal Petr Univ Gandinagar Gujarat India Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Alexandria Univ Fac Sci Dept Math & Comp Sci Alexandria 21511 Egypt

This paper proposes an effective non-dominated moth-flame optimization algorithm (NS-MFO) method for solving multi-objective problems. Most of the multi-objective optimization algorithms use different search techniques inspired by different optimization techniques such as genetic algorithms, differential evolutions, particle swarm optimization, cuckoo search etc., but search techniques of recently developed metaheuristics have hardly been investigated. Non-dominated moth-flame optimization algorithm (NSMFO) is based on the search technique of moth-flame optimization algorithm (MFO) algorithm and utilizes the elitist non-dominated sorting and crowding distance approach for obtaining different non domination levels and to preserve the diversity among the optimal set of solutions respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems and multi-objective engineering design problems with distinctive features. It is shown in this paper that this method effectively generates the Pareto front and also, this method is easy to implement and algorithmically simple. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization moth-flame optimization algorithm Multi-objective engineering design problems

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Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique

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APPLIED SOFT COMPUTING 2017年 59卷 210-222页

作者： Mei, Rebecca Ng Shin Sulaiman, Mohd Herwan Mustaffa, Zuriani Daniyal, Hamdan UMP Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia UMP Fac Comp Syst & Software Engn Gambang 26300 Pahang Malaysia

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature. (C) 2017 Elsevier B.V. All rights reserved.

关键词： moth-flame optimization algorithm Nature-inspired heuristic technique Optimal reactive power dispatch Loss minimization Voltage deviation minimizationa

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Hysteresis model identification for piezoelectric positioning stage using improved moth-flame optimization

Hysteresis model identification for piezoelectric positionin...

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Chinese Automation Congress (CAC)

作者： Zeng, Junhai Li, Zhifu Du, Jiarong Guangzhou Univ Sch Mech & Elect Engn Guangzhou Peoples R China

ISBN: (纸本)9781728176871

The hysteresis existing in the piezoelectric positioning stage seriously affects the performance of the positioning stage, and even causes the instability of the system. However, due to the complexity of hysteresis, hysteresis modeling and parameter identification are still a challenging task. According to the hysteresis characteristic of the piezoelectric positioning stage, a hysteresis parameter identification method based on the improved moth-flame optimization (IMFO) algorithm is proposed. First, the diversity of IMFO is enhanced by differential evolution (DE) algorithm, then the subpopulation update strategy is used to balance the exploration and exploitation capability of the algorithm, so as to effectively improve the performance of the identification algorithm. Finally, for the identification problem of the enhanced Prandll-Ishlinskii (PI) model in the piezoelectric positioning stage, the proposed identification algorithm is compared with five stochastic optimization algorithms. The experimental results show that the proposed algorithm has higher accuracy, which also demonstrates the effectiveness of the algorithm.

关键词： piezoelectric positioning stage parameter identification moth-flame optimization algorithm enhanced Prandl-Ishlinskii model

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Tuning of Power System Stabilizers in Multi-machine Power Systems Using moth flame optimization 6

Tuning of Power System Stabilizers in Multi-machine Power Sy...

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6th International Electrical Engineering Congress (iEECON)

作者： Dey, Prasenjit Bhattacharya, Aniruddha Das, Priyanath Natl Inst Technol Agartala Dept Elect Engn Jirania India

ISBN: (纸本)9781538623176

Power system stabilizers (PSS) are utilized to mitigate power system oscillations by excitation control. This paper proposes a PSS design based on a new population-based algorithm named moth-flame optimization (MFO) algorithm in a multi-machine power system. A conventional speed-based lead-lag PSS is used for design purposes. The parameters of the sine-cosine algorithm based PSS are tuned using MFO to shift the weakly damped electromechanical mode eigenvalues towards the left half of S plane. Results so obtained has been extensively compared with those obtained by Bacteria Foraging (BF) and firefly algorithm (FA) which show the effectiveness of MFO in enhancing overall system stability over an extensive range of loading conditions and fault scenario.

关键词： Eigenvalues moth-flame optimization algorithm Power system stabilizer

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Proposing and Evaluation of SC Techniques for Variable Speed High-Power Operation of SEIG

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IEEE ACCESS 2020年 8卷 20666-20675页

作者： Ahmed, Marwa M. Hassanein, Wael S. Enany, Mohamed A. King Abdulaziz Univ Fac Engn Elect Engn Dept Jeddah 80204 Saudi Arabia King Abdulaziz Univ Fac Engn Ind Engn Dept Jeddah 80204 Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig 44519 Egypt

This work presents a novel methodology for variable speed high power Transfer Capability of a self-excited induction generator (SEIG). The proposed methodology is based on the selection of a suitable firing angle of Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR) for achieving constant rated stator current. WDSEIG would produce a variable speed high power without overheating under variable wind speed and connected load. The analytical approach for the proposed methodology has been implemented to predict the optimal operating firing angle of FC-TCR for full load stator current achievement within the allowed operating range of load and prime mover speed. Also, Soft Computing (SC) techniques have been implemented based on Harmony Search algorithm (HSA), Flower Pollination algorithm (FPA), and moth-flame optimization (MFO) algorithm to achieve the proposed methodology. A comparison between different SC techniques, analytical approach and experimental work are given and evaluated to verify SC techniques accuracy. This evaluation study can be useful in specifying the appropriateness of the SC techniques for High Power Transfer Capability for a SEIG.

关键词： Self-excited induction generator fixed capacitor-thyristor controlled reactor variable speed high power transfer capability moth-flame optimization algorithm flower pollination algorithm harmony search algorithm

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