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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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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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Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第3期10卷 1110-1125页

作者： Mamoudan, Mobina Mousapour Ostadi, Ali Pourkhodabakhsh, Nima Fathollahi-Fard, Amir M. Soleimani, Faezeh Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran 111554563 Iran Univ Bamberg Fac Informat Syst & Appl Comp Sci D-96047 Bamberg Germany Islamic Azad Univ Med Sci Coll Management Sch Adv Sci & technol Tehran *** Iran Univ Victoria Peter B Gustavson Sch Business Victoria BC V8P 5C2 Canada Ball State Univ Dept Math Sci Muncie IN 47306 USA

Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.

关键词： bidirectional gated recurrent unit convolutional neural network firefly algorithm moth-flame optimization algorithm precious metals market prediction

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Preemptive multi-skill resource-constrained project scheduling of marine power equipment maintenance tasks¹

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 5275-5294页

作者： Wang, Peng Lu, Shaojun Cheng, Hao Liu, Lin Pei, Feng Hefei Univ Technol Sch Management Hefei Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL USA

The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm.

关键词： Project scheduling multi-skill preemption moth-flame optimization algorithm ship maintenance

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An echo state network model with the protein structure for time series prediction

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APPLIED SOFT COMPUTING 2024年 153卷

作者： Gong, Yuanpeng Lun, Shuxian Li, Ming Lu, Xiaodong Bohai Univ Coll Math Sci Jinzhou 121013 Liaoning Peoples R China Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China Suqian Univ Coll Informat Engn Suqian 223800 Jiangsu Peoples R China

From the perspective of bionics of biological structure, this paper proposes a new reservoir topology structure with an a -helix form of the secondary protein, named S -ESN. This network model has some advantages compared with the standard leaky -echo state network (Leaky -ESN) model. Because the neurons in the traditional reservoir are randomly and sparsely connected, the stability of the echo state network (ESN) will be reduced, and the prediction accuracy will also be decreased. The S -ESN model proposed greatly improves the internal stability of the reservoir, the dynamic activity of neurons and the prediction accuracy of the ESN. At the same time, the improved moth -flame optimization algorithm (MFO) with the probability of jump disturbance is used to optimize the three parameters: the leakage rate (a), the spectral radius (p), and the input scaling factor (s'"), which can further improve the stability and predictability of the S -ESN. In order to verify the performance of S -ESN, three virtual time series Sin time series with low frequency, Sin time series with high frequency, Mackey -Glass time series (MG) and one practical Sunspot are selected as experimental data. The experimental results show that the S -ESN model has better prediction accuracy.

关键词： The protein structure Time series prediction Echo state network moth-flame optimization algorithm

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Dealing with multi-modality using synthesis of moth-flame optimizer with sine cosine mechanisms

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MATHEMATICS AND COMPUTERS IN SIMULATION 2021年 188卷 291-318页

作者： Chen, Chengcheng Wang, Xianchang Yu, Helong Wang, Mingjing Chen, Huiling Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Chengdu Kestrel Artificial Intelligence Inst Chengdu 611730 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

Evolutionary population-based methods have found their applications in dealing with many real-world simulation experiments and mathematical modelling problems. The moth-flame optimization (MFO) algorithm is one of the swarm intelligence algorithms and it can be used with constrained and unknown search spaces. However, there are still some defects in its performance, such as low solution accuracy, slow convergence, and insufficient exploration capability. This study improves the basic MFO algorithm from the perspective of improving exploration capability and proposes a hybrid swarm-based algorithm called SMFO. The essential notion is to further explore and scan the feature space with taking advantages of the sine cosine strategy. We methodically investigated the efficacy, solutions, and optimization compensations of the developed SMFO using more than a few demonstrative benchmark tests, together with unimodal, multimodal, hybrid and composition tasks, and two widely applied engineering test problems. The simulations point towards this fact that the diversification and intensification inclinations of the original MFO and its convergence traits are fortunately upgraded. The findings and remarks show that the suggested SMFO is a favourable algorithm and it can show superior efficacy compared to other techniques. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： moth-flame optimization algorithm Global optimization Swarm intelligence Sine cosine algorithm

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Multi-objective non-intrusive load disaggregation based on appliances characteristics in smart homes

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ENERGY REPORTS 2021年 7卷 4445-4459页

作者： Fan, Wen Liu, Qing Ahmadpour, Ali Farkoush, Saeed Gholami North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Yeungnam Univ Dept Elect Engn Yeungnam South Korea

The Load Disaggregation (LD) is an optimizing problem. The actual operation states of the appliances would not serve as an optimal solution for a single-objective function, due to various noises as well as frequency interferences from the adjacent systems. In this paper, the LD model with a multi-objective function combines the appliance features at both macro and micro levels. This model contributes a good representation of the appliances from several viewpoints. Recognizing numerous appliances is carried out through five objective functions using apparent, active, and reactive powers, currents, and harmonics as the loads characteristic. The suggested problem is solved utilizing moth-flame optimization (MFO) algorithm with several objectives for LD. Besides, it prevents tuning the weighted parameters and does not ignore the conflict among the objectives. In addition, the Factorial Hidden Markov Model (FHMM) is used to define the allowable modes of the appliances for the next second. It could be resorted to an objective-rank project to cope with the restraint on the number of appliances functioning concurrently. The efficiency of the suggested method for LD is shown by experimental outcomes and is compared with other methods. The results of various combinations of appliances features are evaluated by various evaluation metrics. It is presented that in more features, the results are more accurate. The results show that the accuracy of the proposed method is at least 20 % more than others. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Load disaggregation Factorial hidden Markov model moth-flame optimization algorithm Multi-objective function Smart home appliance Non-intrusive load monitoring

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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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Research on Parameter optimization Method of Sliding Mode Controller for the Grid-Connected Composite Device Based on IMFO algorithm

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SENSORS 2023年第1期23卷 149-149页

作者： Sun, Ji Liu, Jiajun Miao, Miao Lin, Haokun Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China

In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode control (SMC) into GCCD controller. Firstly, the mathematical model of MMC converter is established, and the sliding mode controller is designed based on the SMC principle. Then, aiming at the problems of complex controller structure and difficult parameter tuning in multiple modes of the GCCD, this paper proposes a controller parameter optimization method based on improved Month flame optimization (IMFO) algorithm. This method improves the MFO algorithm by introducing good point set (GPS) initialization and Levy flight strategy, which accelerates the convergence speed of the algorithm while avoiding falling into local optimization, and realizes the optimization of converter controller parameters. Under a variety of standard test functions, the advantages of the proposed IMFO algorithm are verified by comparing it with the traditional algorithm. Finally, in order to realize the automatic tuning of control parameters, the Python-PSCAD joint simulation method is studied and implemented. Taking the comprehensive integral of time and absolute error (CITAE) index as the objective function, the parameters of the sliding mode controller are optimized. The simulation results show that the controller parameters optimized by the IMFO algorithm can make the GCCD have better dynamic performance.

关键词： back-to-back MMC-HVDC grid-connected composite device sliding mode control moth-flame optimization algorithm Python-PSCAD joint simulation

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