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A New Hybrid Machine Learning Approach for Prediction of Phenanthrene Toxicity on Mice

IEEE ACCESS 2019年 7卷 138461-138472页

作者： Xu, Yueting Yu, Keting Wang, Pengjun Chen, Huiling Zhao, Xuehua Zhu, Jiayin Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Med Univ Sch Clin Med Sci 1 Sch Informat & Engn Wenzhou 325035 Peoples R China Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Guangdong Peoples R China Wenzhou Med Univ Lab Anim Ctr Wenzhou 325035 Peoples R China

Phenanthrene, a PAH with three fused benzene rings, is usually used as a model for the study on PAHs. During 4 days, 166 male mice were equally and randomly divided into two groups. One group was given vehicle-corn oil by oral gavage, the other was given phenanthrene at a dose of 450 milligrams per kilogram per day. In this study, in order to predict mice's phenanthrene poisoning by virtue of blood analysis indices, a new machine learning approach was put forward, which was based on an improved binary moth flame optimizer combined with extreme learning machine. The results of the experiment have manifested that the blood analysis indices of the control and phenanthrene groups were significantly different (p < 0.5). The most important correlated indices including serum alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), plateletcrit (PCT) and red blood cell distribution width-standard deviation (RDW-SD) were screened through feature selection. The classification results demonstrated that the proposed method can achieve 93.38% accuracy and 98.33% specificity. Promisingly, there is a new and accurate way to detect the status of phenanthrene poisoning expectably.

关键词： Phenanthrene hepatotoxicity moth-flame optimization algorithm feature selection extreme learning machine

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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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Layout optimisation of offshore wave energy converters using a novel multi-swarm cooperative algorithm with backtracking strategy: A case study from coasts of Australia

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ENERGY 2022年第PartE期239卷 122463-122463页

作者： Neshat, Mehdi Mirjalili, Seyedali Sergiienko, Nataliia Y. Esmaeilzadeh, Soheil Amini, Erfan Heydari, Azim Garcia, Davide Astiaso Univ Adelaide Sch Comp Sci Optimisat & Logist Grp Adelaide SA Australia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Adelaide Sch Mech Engn Adelaide SA Australia Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE Rome Italy Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15%, 3.29%, 3.62%, 9.2%, 5.74%, and 2.43% for

关键词： Cooperative co-evolution algorithms Multi-swarm optimisation Equilibrium optimisation Multi verse optimiser Meta-heuristics Position optimisation Renewable energy Wave energy converters Particle swarm optimization Whale optimization algorithm moth-flame optimization algorithm algorithm optimization

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Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2022年第0期126卷 1页

作者： Zhou, Jian Shen, Xiaojie Qiu, Yingui Shi, Xiuzhi Khandelwal, Manoj Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia

Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In this research, metaheuristic algorithms are used to improve the performance of cross-correlation stacking (CCS). CCS has able to provide excellent location accuracy as it uses more information in the entire waveform for location. However, this method often requires more calculation time due to its complex mathematical modeling. To overcome this problem, various metaheuristic algorithms (i.e. moth flame optimization (MFO), ant lion optimization (ALO) and grey wolf optimization (GWO)) have been used to improve CCS. It has been found that appropriate control parameters can improve the metaheuristic algorithm performance manyfold. So, these control parameters have been adjusted based on three different perspectives, i.e. success rate (SR), computational efficiency and convergence performance. The results show that these models are able to provide better location efficiency compared to the full grid search (FGS) and particle swarm optimization (PSO) based on ensuring good location accuracy. It is also found that MFO is significantly better than the other metaheuristic algorithms. In addition, the superiority of CCS over traditional location methods is verified through comprehensive tests, and the influence of the speed model and the number of sensors on the location performance of CCS was tested.

关键词： Microseismic location method Cross-correlation stacking moth-flame optimization algorithm Ant Lion Optimizer Grey Wolf Optimizer

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MFeature: Towards high performance evolutionary tools for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115655-115655页

作者： Xu, Yueting Huang, Hui Heidari, Ali Asghar Gui, Wenyong Ye, Xiaojia Chen, Ying Chen, Huiling Pan, Zhifang Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China South China Univ Technol Guangzhou 510000 Peoples R China Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China Nanchang Hangkong Univ Sch Software Nanchang 330063 Jiangxi Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China

Feature selection commonly refers to a process of using the candidate algorithm to detect the optimal feature sets during the preprocessing steps in machine learning and data mining. This procedure can optimize the dataset's features to be analyzed and maximize the classification performance based on the selected optimal feature combination. In this work, a hybridization model is developed and utilized to select optimal feature subset based on an innovative binary version of moth-flame optimizer and the K-Nearest Neighbor Classifier (KNN) for classification tasks. The proposed new technique, abbreviated as MFeature or ESAMFO, applies several strategies, including two types of transfer functions, ensemble strategy, simulated annealing (SA) disturbance mechanism, and crossover scheme to improve the equilibrium between the global exploration and local exploitation capabilities of the basic MFO. Each individual in the proposed algorithm is evaluated by the size of selected features and the error rate of the KNN classifier. The proposed model's efficacy is assessed on 30 benchmark datasets with different dimensions from the UCI repository and compared with other KNN based feature selection algorithms from the literature. The comprehensive results via various comparisons reveal the efficiency of the proposed technique in decreasing the classification error rate compared with other feature selection algorithms, ensuring the capability of ESAMFO in exploring the feature space and selecting the most informative features for classification purposes. For post publications that support this research, readers can refer to https://aliasgharheidari. com.

关键词： moth-flame optimization algorithm Crossover Ensemble Simulated annealing Feature selection Data classification

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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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Ameliorated moth-flame algorithm and its application for modeling of silicon content in liquid iron of blast furnace based fast learning network

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APPLIED SOFT COMPUTING 2020年 94卷 106418-106418页

作者： Zhao, Xiaodong Fang, Yiming Liu, Le Xu, Miao Zhang, Pan Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Res Ctr Educ Minist Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China

moth-flame optimization (MFO) algorithm is a widely used nature-inspired optimization algorithm. However, for some complex optimization problems, such as high dimensional and multimodal problems, the MFO may fall into the local optimal solution. Hence, in this paper an ameliorated moth-flame optimization (AMFO) algorithm is presented to improve the solution quality and global optimization capability. The key features of the proposed algorithm are the Gaussian mutation produce flames and the modified position updating mechanism of moths, which can improve the ability of MFO to jump out of local optimum solutions. In addition, opposition-based learning is adopted to initialize the population. The AMFO algorithm is compared with 9 state-of-the-art algorithms (such as Levy moth-flame optimization (LMFO), Grey Wolf optimization (GWO), Sine Cosine algorithm (SCA), Heterogeneous Comprehensive Learning Particle Swarm optimization (HCLPSO)) on 23 classical benchmark functions. The comparative results show that the AMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Furthermore, the AMFO is adopted to optimize the parameters of fast learning network (FLN) to build the prediction model of silicon content in liquid iron for blast furnace, and simulation experiment results from field data show that the root mean square error of the AMFO-FLN model is 0.0542, hit ratio is 91 and the relative error is relatively stable, the main fluctuation is between-0.1 and 0.1;compared with other ten silicon content in liquid iron models, the AMFO-FLN model has better predictive performance. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： moth-flame optimization algorithm Fast learning network Silicon content Blast furnace

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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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Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach

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ENERGY 2021年 235卷 121407-121407页

作者： Liu, Zhi-Feng Li, Ling-Ling Liu, Yu-Wei Liu, Jia-Qi Li, Heng-Yi Shen, Qiang Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China

This study contributes to construct the mathematical model of hybrid dynamic economic emission dispatch (HDEED) considering renewable energy generation and propose a novel solving approach based on enhanced moth-flame optimization algorithm. Renewable energy power generation technology has an important impact on reducing pollutant emissions and promoting sustainable development. There-fore, this study aims to investigate the HDEED problem in consideration of renewable energy generation and improve the economic and environmental benefits of the power system. First, a moth-flame opti-mization algorithm based on position disturbance updating strategy (MFO_PDU) was proposed aiming at the non-convex, non-linear and high-dimensional characteristics of HDEED problem. Second, the mathematical model of HDEED on the basis of Wind-Solar-Thermal integrated energy was constructed, while taking into account the valve point effect, equality constraints and inequality constraints. Finally, three cases including test systems of different scales were formulated and employed to verify the pro-posed approach, and the compromise solution was determined through membership function. The re-sults revealed that the fuel cost obtained by the MFO_PDU algorithm was 11.31%, 4.01% and 5.27% smaller than those of HHO, TSA and MFO algorithms for small-scale test system. Accordingly, the research outcomes contribute in reducing the fuel cost and pollutant emissions of power generation system, and further improving the utilization and penetration rate of renewable energy. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Renewable energy Multi-objective optimization Dynamic economic emission dispatch moth-flame optimization algorithm Sustainable development

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