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An evaluation of different bio-inspired feature selection techniques on multivariate calibration models in spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 246卷 119042页

作者： El-Zeiny, Mohamed B. Zawbaa, Hossam M. Serag, Ahmed Modern Univ Technol & Informat MTI Analyt Chem Dept Fac Pharm Cairo Egypt Beni Suef Univ Fac Comp & Informat Bani Suwayf Egypt Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Al Azhar Univ Pharmaceut Analyt Chem Dept Fac Pharm Cairo 11751 Egypt

Herein, two new swarm intelligence based algorithms namely;grey wolf optimization (GWO) and antlion opti-mization (ALO) algorithms were presented, for the first time, as variable selection tools in spectroscopic data analysis. In order to assess the performance of these algorithms, they were applied along with the recently introduced firefly algorithm (FFA) and the well-established genetic algorithm (GA) and particle swarm optimization (PSO) algorithm on four different spectroscopic datasets of varying sizes and nature (UV and IR). Partial least squares (PLS) regression models were built using the selected variables by these algorithms along with the full spectral data as the reference models. The obtained results prove that the ALO and GWO optimization algorithms select variables in most cases less than GA and PSO while keeping the PLS performance almost the same. Accordingly, these algorithms can be successfully used for variable selection in spectroscopic data analysis. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Antlion optimization Grey wolf optimization firefly algorithm Genetic algorithm Feature selection Spectroscopy

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Hosting capacity enhancement of renewable-based distributed generation in harmonically polluted distribution systems using passive harmonic filtering

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 44卷

作者： Bajaj, Mohit Singh, Amit Kumar Natl Inst Technol Delhi Dept Elect & Elect Engn New Delhi 110040 India

In spite of being economically viable for numerous applications, renewable energy cannot realise its true penetration potential until some of the barriers are not tackled with excellence. Such barriers are harmonic distortion, distribution cable's ampacity and voltage rise limits, those put bound on the maximum allowable penetration level of renewable energy. This paper formulates the enhancement of power quality-constrained hosting capacity (HC) as a multi-objective optimization (MOO) problem under several constraints of system's performance indices (PIs). The considered performance indices are individual order and total harmonic distortion in the line current and point of common coupling (PCC)'s voltage, load power factor (PF), distribution line's ampacity and steady-state voltage profile. In the formulated multi-objective optimization model, an optimal design of a third-order damped filter and size of the distributed generation (DG) unit is simultaneously determined for achieving maximum hosting capacity and power factor at the PCC while keeping system's other indices such as total voltage harmonic distortion (TVHD) and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed Pareto-based multi-objective firefly algorithm (MOFA). The extension of the multi-objective firefly algorithm is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives by plotting the same on different 2-axis planes. The optimization efficiency and superiority of the proposed multi-objective firefly algorithm based hosting capacity enhancement approach is validated by comparing the results with those obtained by popular multi-objective PSO (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) under similar objectives. Also, the results of the proposed methodology are compared with those achieved from one of the most recently introduced hosting capacity e

关键词： Background distortion Distributed generation firefly algorithm Harmonic analysis Hosting capacity Multi-objective optimization Passive power filters

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A novel bio-inspired stochastic framework to solve energy management problem in hybrid AC-DC microgrids with uncertainty

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2021年第3期18卷 165-175页

作者： Shojaeiyan, Sattar Niknam, Taher Nafar, Mehdi Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran

This paper introduces an effective intelligent framework for the optimal energy management of hybrid AC-DC microgrids in the presence of varied renewable energy sources and storage units. In order to consider the uncertainty effects, a new stochastic framework based on cloud drops and fuzzy set theory is devised. In the proposed stochastic scheme, the uncertainties of the forecast error in the wind unit output power, photovoltaics output power, electrical power demands and the market price are modelled. Such a model reveals an entropy-entropy concept which can help for capturing the standard deviation uncertainty of the uncertain parameters. In accordance with the high complexity of the proposed formulation, a new optimisation algorithm based on modified firefly algorithm and a three-phase modification method is proposed. The high capability and effectiveness of the proposed framework is assessed using a revised IEEE test system.

关键词： evolutionary algorithm energy management problem hybrid AC-DC microgrid firefly algorithm cloud theory uncertainty optimisation

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Internet of things based smart city design using fog computing and fuzzy logic

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SUSTAINABLE CITIES AND SOCIETY 2022年 79卷

作者： Bhardwaj, Kartik Krishna Banyal, Siddhant Sharma, Deepak Kumar Al-Numay, Waleed Netaji Subhas Univ Technol Dept Instrumentat & Control New Delhi India Netaji Subhas Inst Technol New Delhi India Netaji Subhas Univ Technol Dept Informat Technol New Delhi India King Saud Univ Comp Sci Dept Riyadh Saudi Arabia

The prevalence of IoT based technology in contemporary society allows for the planning of smart city infrastructure with individually owned devices as participating nodes in a social smart network. Edge computing application into such an IoT network facilitates the distributed computing framework by bringing the computational application closer to the end-users in form of Fog sub-networks, comprising the local participating end user IoT nodes as service requesters and specialized servers catering to the requests, modelled for the infrastructure category they are delivering (Smart healthcare, ITS, Smart architecture, etc.). Opportunistic-based last mile connectivity between the Fog servers and participating nodes aides the dynamicity of the topology for efficient information. Aiding the same, the proposed edge computing enabled, sustainable, opportunistic IoTbased intuitive data exchange, collection and operation scheme that employs a dynamic routing procedure that considers human interactions, their movement patterns and their decision-making process. The proposed scheme employs probabilistic history-based parameters that keep track of node interactions. The interactions are modelled on the firefly PSO, allowing exploitation of the social mobility. The Fuzzy logic is used for hop decision making based on the social and attribute-based parameters which mimic intuitive and pragmatic decision making of humans in terms of social interactions and mobility.

关键词： Internet of things Opportunistic networks Smart city architecture Edge computing Fog computing firefly algorithm Fuzzy logic Nature inspired algorithm Probabilistic routing

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Using a hybrid artificial intelligence method for estimating the compressive strength of recycled aggregate self-compacting concrete

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2022年第12期26卷 5569-5593页

作者： Pazouki, Gholamreza Pourghorban, Arash Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Univ Art Fac Architecture & Urbanism Dept Architecture & Energy Tehran Iran

Nowadays, because of human activities, the earth's environment is in danger. So, all industries including construction and building industries should be concerned about the environment, and their activities have to be in this direction. Using waste materials in construction is one of the solutions for decreasing environmental damage, for example;using recycled aggregate in various types of concrete including self-compacting concrete (SCC). Moreover, the usage of a non-destructive method for determining the mechanical properties of concrete can improve the environmental situation. To do so, in this study the radial basis function neural network (RBFNN) assisted by firefly algorithm (FA) is proposed for predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC). In these regards, the information of 310 samples of RASCC has been collected from previous studies. In this model, the water to binder ratio, age, the ratio of recycled coarse aggregate, coarse aggregate, fine aggregate, and superplasticizer have been considered as input variables, and compressive strength as the output variable. Also, an ANN model has been utilized to conduct comparisons. The performance of the models has been evaluated based on statistical parameters, and by comparing the results of the model with experimental results. The values of statistical parameters of RBFNN model (for all data: R-values:0.97 & RMSE:3.3) show that the correlation between results of the model and experimental results are high and the error of the model's results are acceptable. Moreover, results of the models indicate that both models have good ability and acceptable accuracy for predicting the compressive strength of RASCC.

关键词： Recycled aggregate self-compacting concrete radial basis function neural network firefly algorithm artificial neural network compressive strength

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The multiple trip vehicle routing problem with backhauls in random fuzzy environment: using (?,?)-cost minimization model under the Hurwicz criterion

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2019年第12期96卷 2548-2566页

作者： Rahmani, Arsalan Univ Kurdistan Dept Math Pasdaran StrPOB 416 Sanandaj *** Iran

In the literature on VRP issues, many works have been studied in deterministic environments. Such proposed models cannot show the appropriate demands of different customers in the real world. One of the most important VRP problems that are widely applicable in real life is nondeterministic multiple trip vehicle routing problem with backhauls (MT-VRPB). In this paper, we focus on MT-VRPB in the random fuzzy environment using cost minimization model under the Hurwicz criterion. This model can deal with various MT-VRPB in random, fuzzy and random fuzzy environments. Regarding to the structure of the proposed model and in order to solve it, a hybrid intelligent algorithm based on the integration of the simplex algorithm, fuzzy simulations, and a firefly algorithm is proposed. The effectiveness and sensitive analysis of the proposed algorithm is demonstrated by solving some numerical examples.

关键词： Random fuzzy environment fuzzy simulation firefly algorithm multiple trip vehicles routing problem Hurwicz criterion 90Cxx

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Handwritten optical character recognition by hybrid neural network training algorithm

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IMAGING SCIENCE JOURNAL 2019年第7期67卷 359-373页

作者： Sampath, A. K. Gomathi, N. Rizvi Coll Engn Dept Comp Engn Mumbai Maharashtra India Veltech Dr RR & Dr SR Tech Univ Chennai Tamil Nadu India

Handwritten optical character recognition (OCR) is the renowned research area in several fields, like writers identification, bank cheques, and so on. Literature works presented the handwritten OCR for various languages. This paper proposes a hybrid neural network training algorithm for English handwritten OCR. Initially, the noise in the input image is removed using the median filter, and the image is resized. Then, the feature sets, positional, and structural descriptors are extracted from the input image. Once the feature sets are extracted, the proposed FLM-based neural network identifies the handwritten character. The FLM proposed by combining the firefly and the Levenberg-Marquardt (LM) algorithm for training the neural network. Finally, the proposed FLM-based neural network is integrated within the feed forward neural network, and the classification of character is done with 95% accuracy based on the size of training data, number of hidden neurons and number of hidden layers.

关键词： FLM based neural network H-descriptors D-descriptors firefly algorithm Levenberg-Marquardt algorithm Optical Character Recognition median filter Feed forward neural network

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Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression" by Jui-Sheng Chou, Ngoc-Tri Ngo, and Anh-Duc Pham (vol 30, 04015002, 2015)

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2016年第1期30卷

作者： Chou, Jui-Sheng Ngoc-Tri Ngo Anh-Duc Pham Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei 10607 Taiwan Univ Danang Univ Sci & Technol Fac Project Management Danang 59000 Vietnam

The shear strength of reinforced concrete (RC) deep beams is a dynamic phenomenon that varies with many mechanical and geometrical factors. Accurately estimating shear strength in RC deep beams is a vital issue in engineering design and management. However, prediction accuracy is still poor. This study presents a nature-inspired metaheuristic regression method for accurately predicting shear strength in RC deep beams that combines a novel smart artificial firefly colony algorithm (SFA) and least squares support vector regression (LS-SVR). The SFA integrates the firefly algorithm (FA), chaotic map (CM), adaptive inertia weight (AIW), and Lévy flight (LF). First, an adaptive approach and randomization methods (i.e., CM, AIW, and LF) were incorporated in FA to construct an effective metaheuristic algorithm for global optimization. The SFA was then used to optimize the hyperparameters of the LS-SVR model. The proposed model was constructed using a data set for RC deep beams which was derived from the literature. Model performance was evaluated by comparing results with those of a baseline SVR model and with previous methods via a cross validation algorithm. Analytical results show that the novel optimized prediction model is superior to others in predicting the shear strength of RC deep beams and that it can assist civil engineers in designing RC deep beam structures.

关键词： Deep beams Evolutionary optimization firefly algorithm Nature-inspired metaheuristic algorithm Reinforced concrete Shear strength Support vector regression

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Control of electric machines using flower pollination algorithm based fractional order PID controller

Global Transitions Proceedings

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Global Transitions Proceedings 2021年第2期2卷 227-232页

作者： Tirumalasetty Chiranjeevi Naladi Ram Babu Ankit Yadav Vijay Kumar Das Shyama Charan Prasad Amardeep Sonkar Sachin Kumar Verma REC Sonbhadra Uttar Pradesh India NIT Silchar Assam India

In this work, fractional order proportional integral derivative controller (FOPIDC) is proposed and implemented for speed control of electric machines using nature-inspired metaheuristic optimization algorithm (MOA) named as flower pollination algorithm (FPA). Investigations are performed by considering the second-order transfer function of D.C Motor. FOPIDC constants K P , K I , K D , λ , and μ are optimized by considering FPA. The desired response of the electric machine is obtained by using tuned parameters of FOPIDC. After that, FOPIDC response using FPA is compared with PIDC response using FPA in the sense of time-domain specifications and performance indices (PI) like ‘integral of square error (ISE), integral of time-weighted square error (ITSE), integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE)’. Finally, the desired response of electric machine using FPA based FOPIDC is compared with desired response of electric machine using firefly algorithm (FA) and particle swarm optimization (PSO) algorithm based FOPIDC.

关键词： D. C Motor speed control firefly algorithm Flower pollination algorithm FOPID controller performance indices PSO

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A hybrid optimization-based deep belief neural network for the classification of vegetation area in multi-spectral satellite image

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2020年第4期24卷 363-379页

作者： Gavade, Anil B. Rajpurohit, Vijay S. KLS Gogte Inst Technol Belagavi 590008 Karnataka India

Over the last few decades, multiple advances have been done for the classification of vegetation area through land cover, and land use. However, classification problem is one of the most complicated and contradicting problems that has received considerable attention. Therefore, to tackle this problem, this paper proposes a new firefly-Harmony search based Deep Belief Neural Network method (FHS-DBN) for the classification of land cover, and land use. The segmentation process is done using Bayesian Fuzzy Clustering,and the feature matrix is developed. The feature matrix is given to the proposed FHS-DBN method that distinguishes the land coverfrom the land use in the multispectral satellite images, for analyzing the vegetation area. The proposed FHS-DBN method is designedby training the DBN using the FHS algorithm, which is developed by the combination of firefly algorithm (FA) and Harmony Search (HS) algorithm. The performance of the FHS-DBN model is evaluated using three metrics, such as Accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). From the experimental analysis, it is concludedthat the proposed FHS-DBN model achieves ahigh classification accuracy of 0.9381, 0.9488, 0.9497, and 0.9477 usingIndian Pine, Salinas scene, Pavia Centre and university, and Pavia University scene dataset.

关键词： Classification multi-spectral satellite image firefly algorithm harmony Search algorithm DBN vegetation index

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