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Structural damage detection based on MAC flexibility and frequency using moth-flame algorithm

STRUCTURAL ENGINEERING AND MECHANICS 2019年第6期70卷 649-659页

作者： Ghannadi, Parsa Kourehli, Seyed Sina Islamic Azad Univ Ahar Branch Dept Civil Engn Ahar Iran

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using moth-flame optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

关键词： moth-flame optimization algorithm flexibility frequency

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Design of phase-only reconfigurable planar array antenna in selected phi cuts using various meta-heuristic optimization algorithms

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2019年第4期44卷 1-8页

作者： Jamunaa, D. Mahanti, G. K. Al Attar, Feras Nadhim Hasoon Natl Inst Technol Dept Elect & Commun Engn Durgapur 713209 India NUST Coll Engn Dept Elect & Comp Engn Muscat Oman

In this paper, a pattern synthesis based on a multiobjective optimization algorithm is proposed for the generation of a reconfigurable pencil/flat top dual-beam planar antenna array built using isotropic antenna elements in selected phi cuts. These beams claim the same amplitude excitations and differ from each other in phase excitations. Zero-phase excitations are used in pencil beam and these phases are updated with optimum phases for the flat top beam. All the excitations are obtained using moth-flame optimization algorithm. With the support of the fitness functions, care is taken to control the expected values of the radiation pattern parameters to remain under certain fixed limit. In addition, synthesis is also done for the provision of a null in a particular direction for rejection of interference in the pencil beam in two different phi cuts. To suppress the mutual coupling effects, dynamic range ratio is kept under a threshold limit. Simulation results show the effectiveness of this proposed synthesis for phi cut planes. This algorithm is compared and proved to be better in many aspects over the standard meta-heuristic algorithms like Artificial Bee Colony and Imperialist Competitive algorithms in terms of performance parameters.

关键词： Phase-only reconfigurable array antenna Side Lobe Level flat top beam moth-flame optimization algorithm Artificial Bee Colony algorithm Imperialist Competitive algorithm

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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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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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Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs

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JOURNAL OF HYDROLOGY 2019年 569卷 12-21页

作者： Zounemat-Kermani, Mohammad Mahdavi-Meymand, Amin Shahid Bahonar Univ Kerman Water Engn Dept Kerman Iran

Piano key weirs (PKWs) are acquired and developed for free surface control structures which improve their performance by increasing the storage capacity and flood evacuation. In this study, the potential combinations of two popular artificial intelligence data-driven models (Al-DDMs) of multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS) with four meta-heuristic optimization algorithms (particle swarm optimization, genetic algorithm, firefly algorithm & moth-flame optimization) are assessed for predicting the PKW's flow rate. Comparing the outcomes of the ten standard and hybrid Al-DDMs with three empirical relations based on several statistics and diagnostic analysis (such as the Taylor diagram) for estimating the flow rate shows that the AI-DDMs can predict the passing flow more accurately. In addition, the particle swarm optimization and firefly algorithm meta-heuristic algorithms improve the performance of ANFIS and MLPNN, respectively. The Mann-Whitney test for investigating the differences between two independent applied models indicates a significant difference between the Al-DDMs and two of the empirical relations at the 95% confidence level.

关键词： Free surface flow Sharp crested weirs Firefly algorithm moth-flame optimization algorithm Taylor diagram

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Modeling of electricity demand forecast for power system

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 6857-6875页

作者： Jiang, Ping Li, Ranran Lu, Haiyan Zhan, Xiaobo Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sch Software Sydney NSW Australia

The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.

关键词： Electricity demand forecasting Adaptive Fourier decomposition Seasonal adjustment moth-flame optimization algorithm Machine learning method

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Meta-heuristic matrix moth-flame algorithm for optimal reconfiguration of distribution networks and placement of solar and wind renewable sources considering reliability

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ENVIRONMENTAL TECHNOLOGY & INNOVATION 2020年 20卷

作者： Jafar-Nowdeh, Ali Babanezhad, Manoochehr Arabi-Nowdeh, Saber Naderipour, Amirreza Kamyab, Hesam Abdul-Malek, Zulkurnain Ramachandaramurthy, Vigna K. Gorgan Azad Univ Dept Elect Engn Gorgan Golestan Iran Golestan Univ Fac Sci Dept Stat Gorgan Golestan Iran Golestan Tech & Vocat Training Ctr Gorgan Golestan Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Univ Teknol Malaysia Fac Engn Sch Elect Engn Inst High Voltage & High Current Johor Baharu 81310 Malaysia Univ Teknol Malaysia Razak Fac Technol & Informat Engn Dept Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Univ Tenaga Nas Inst Power Engn Dept Elect Power Engn Jalan Ikram Uniten Kajang 43000 Malaysia

This paper presents optimal multi-criteria reconfiguration of radial distribution systems with solar and wind renewable energy sources using the weight factor method while considering reliability. Minimizing the power loss, improving the voltage profile and stability of the system, as well as, enhancing the reliability are the main objective functions of the problem to address. The reliability index is assumed as the energy not-supplied (ENS) of the end-users. Optimized variables of the problem include opened lines of the system in the reconfiguration process to maintain the radial structure of the network along with finding the optimal place and size of photovoltaic (PV) systems and wind turbine (WT) units in the distribution system, which are determined based on a new meta-heuristic called moth-flame optimization (MFO) algorithm. Simulations for different scenarios are performed utilizing reconfiguration and placement of renewable sources on an IEEE 33-bus radial distribution system. Obtained results in solving the problem indicate the superiority of the presented method compared with some methods in the literature. Furthermore, the results showed that the combined method as the reconfiguration and WT placement simultaneously bring the best performance for the network with lower power loss, improved voltage profile and stability, and enhanced reliability. Moreover, the results showed that considering reliability helps significantly reduce the energy not-supplied of the customers and supply their maximum load demand. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Radial distribution system Renewable energy sources Voltage profile and stability Reliability 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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Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 191卷 106541-106541页

作者： Adnan, Rana Muhammad Mostafa, Reham R. Islam, Abu Reza Md. Towfiqul Kisi, Ozgur Kuriqi, Alban Heddam, Salim Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Mansoura Univ Fac Comp & Informat Sci Informat Syst Dept Mansoura 35516 Egypt Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh Univ Appl Sci Dept Civil Engn D-23562 Lubeck Germany Ilia State Univ Dept Civil Engn Tbilisi 0162 Georgia Univ Lisbon Inst Super Tecn CERIS Lisbon Portugal Univ 20 Aout 1955 Hydraul Div Agron Dept Fac Sci Route El HadaikBP 26 Skikda Algeria

Hybrid heuristic algorithm (HA), an innovative technique in the machine learning field, enhances the accuracy of reference evapotranspiration (ETo) prediction, which is of paramount significance for regional water management, agricultural planning, and irrigation designing. However, the new hybrid HA techniques, namely moth-flame optimization algorithm (MFO) and Water Cycle optimization algorithm (WCA) are rarely applied to estimate ETo in the earlier literature. Therefore, this study assessed prediction and the estimation abilities of a novel hybrid adaptive neuro-fuzzy inference system (ANFIS-WCAMFO) for monthly ETo of Dhaka and Mymensing stations with data-limited humid regions of south-central Bangladesh. Prediction precision of the ANFIS-WCAMFO model is compared with other state-of-art models, i.e., ANFIS-WCA and ANFIS-MFO using a 4-fold cross-validation method including root-mean-square-error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2). Nine input combinations of meteorological datasets, including extraterrestrial radiation (Ra), solar radiation (Rs), maximum and minimum temperatures (Tmax and Tmin), relative humidity (RH), and wind speed (U2), were employed for model training and testing purposes. The ANFIS-WCAMFO performed superior to the other state-of-arts methods in estimating monthly ETo in all input combinations. The ANFIS-WCA, ANFIS-MFO, and ANFIS-WCAMFO hybrid models for estimating ETo improved RMSE as 2.7%, 6.9%, and 15.1% for Dhaka station and 0.6%, 7.3%, 12.4% for Mymensingh station, respectively. The use of the Ra variable with the temperature inputs considerably improved the models' accuracy in ETo;improvements in RMSE, MAE, NSE, and R-2 of the hybrid ANFIS-WCMFO models were 26.6%, 30.9%, 17.6%, and 8.2% for Dhaka station and by 28.8%, 34.2%, 18.8% and 22.1% for Mymensingh station, respectively. The proposed hybrid neuro-fuzzy model has been suggested as a promising technique due t

关键词： Reference evapotranspiration prediction Adaptive neuro-fuzzy inference system moth-flame optimization algorithm Water cycle optimization algorithm Hybrid heuristic algorithms

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