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Radial basis function neural network-based face recognition using firefly algorithm

NEURAL COMPUTING & APPLICATIONS 2018年第8期30卷 2643-2660页

作者： Agarwal, Vandana Bhanot, Surekha BITS Dept Comp Sci & Informat Syst Pilani 333031 Rajasthan India BITS Dept Elect & Elect Engn Pilani 333031 Rajasthan India

This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma () of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition.

关键词： Face recognition Radial basis function neural network firefly algorithm RBF center selection

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Application of Hybrid firefly algorithm for Sheath Loss Reduction of Underground Transmission Systems

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IEEE TRANSACTIONS ON POWER DELIVERY 2013年第4期28卷 2085-2092页

作者： Huang, Shyh-Jier Liu, Xian-Zong Su, Wei-Fu Yang, Shu-Hsuan Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Kun Shan Univ Dept Elect Engn Tainan 71003 Taiwan Taiwan Power Co Taipei 10016 Taiwan

This paper proposes a hybrid firefly algorithm (HFA) to assist in decision-making for reactor arrangement in underground cable transmission systems. The HFA method is proposed based on the analysis of phototaxis behavior of fireflies, and enables solving the optimization problem effectively. In this study, by formulating high relationships among connected reactors, sheath loss, and induced voltage, the HFA method is employed to determine the appropriate reactor placement in an underground transmission systems. Through the tests made on different transmission lines along with the results compared to other methods, the proposed approach provides satisfactory decision support for reactor placement and serves as a beneficial reference for underground transmission planning and design.

关键词： firefly algorithm reactor placement sheath loss underground cable

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Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2020年第4期30卷 1119-1131页

作者： Pugalenthi, R. Oliver, A. Sheryl Anuradha, M. St Josephs Coll Engn Dept CSE Chennai 600119 Tamil Nadu India

Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.

关键词： firefly algorithm fuzzy interference system impulse noise Mamdani particle swarm optimization

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Assessment of ramping cost for independent power producers using firefly algorithm and gray wolf optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4479-S4490页

作者： Kathiravan, K. Prabha, N. Rathina Theni Kammavar Sangam Coll Technol Dept Elect & Elect Engn Theni 625534 Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 Tamil Nadu India

In Deregulated Environment, all the independent power producers (IPP) are clustered in nature and they were operated in unison condition to meet out the cluster load demand of various levels of consumers in continuous 24 hours horizon. These IPP were respond and reschedule their clustered operating units with time confine among the reliant conditions like incremental in overall consumer demand, credible contingency and wheeling trades. Amid this process, the ramping cost is acquired during the incidence of any infringement in the secured elastic limit or Ramp rate limits. In this paper, optimal operating cost of the independent power producer is incurred with ramping cost considering stepwise and piecewise slope ramp rate utilizing firefly algorithm and Gray wolf optimization algorithm. Optimal power flow is carried out for the three standard test systems: five, six and ten power producers are having secured elastic limits are taken for computation in Matlab environment.

关键词： firefly algorithm Gray wolf optimization Piecewise linear ramp rate Independent power producer Optimal power flow Ramp rate limits

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Embedding firefly algorithm in optimization of CAPP turning machining parameters for cutting tool selections

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 135卷 317-325页

作者： Zubair, Ahmad Faiz Abu Mansor, Mohd Salman Univ Sains Malaysia Sch Mech Engn Engn Campus Perai 14300 Pulau Pinang Malaysia

The intervention of human expert to best select proper machining parameters can be reduced by automatic process planning. This includes such tedious work and sufficient knowledge one has to distinguish in choosing various options of cutting tools from the manufacturer's catalogue. In this work, cutting tool selections of turning machining workpiece by optimizing machining parameters were done. Turning machining features were automatically recognized by volume decomposition method and sub-delta volumes (SDV) were generated. Geometrical data extracted from SDV were then utilized in getting optimum machining parameters by using the firefly algorithm (FA). Machining parameters including cutting speed (CS), feed rate (f) and depth of cut (d) were optimized within an objective function of minimizing unit production cost (UPC). A relevant algorithm embedded with FA is proposed to execute the computer-aided process planning (CAPP) system. The feasibility of the approach and the developed algorithm was verified through an illustrative case study of an industrial part model.

关键词： CAPP Volume decomposition firefly algorithm Cutting tool selections Turning machining

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Line utilisation factor-based optimal allocation of IPFC and sizing using firefly algorithm for congestion management

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第1期10卷 115-122页

作者： Mishra, Akanksha Gundavarapu, Venkata Nagesh Kumar GITAM Univ GITAM Inst Technol Dept EEE Visakhapatnam Andhra Pradesh India

As a consequence of privatisation and deregulation of the electric power market, there is a marked increase of scheduled power that flows in the transmission line and also the spontaneous power exchanges leading to complex power transmission congestion problems. The proper placement of interline power flow controller (IPFC) can improve the transmission line congestion problem to a great extent. This study proposes a method for optimal placement of IPFC based on disparity line utilisation factor (DLUF) and firefly algorithm (FA)-based optimal tuning for a multi-objective function to control the congestion in transmission lines. DLUF determines the difference between the percentages mega voltage ampere (MVA) utilisation of each line connected to the same bus. The IPFC is placed in the lines with maximum DLUF. This method has been implemented on an IEEE 30-bus system and the results have been presented and analysed. Optimal tuning of IPFC at the proposed location is carried out using FA for a multi-objective function consisting of active power loss, total voltage deviations, security margin and capacity of the installed IPFC. The tuning of IPFC is also carried out using genetic algorithm. The results obtained have been compared with that of FA for different loading conditions.

关键词： load flow power convertors power system management power markets power transmission lines IEEE standards power system security genetic algorithms line utilisation factor-based optimal allocation interline power flow converter firefly algorithm electric power market privatisation electric power market deregulation spontaneous power exchange power transmission congestion management IPFC disparity line utilisation factor DLUF FA-based optimal tuning multiobjective function transmission line congestion control IEEE 30-bus system active power loss security margin genetic algorithm

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Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm

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NEURAL COMPUTING & APPLICATIONS 2018年第6期29卷 269-281页

作者： Faradonbeh, Roohollah Shirani Armaghani, Danial Jahed Amnieh, Hassan Bakhshandeh Mohamad, Edy Tonnizam Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran UTM Dept Geotech & Transportat Fac Civil Engn Skudai 81310 Johor Malaysia Univ Tehran Sch Min Coll Engn Tehran 111554563 Iran

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock.

关键词： Flyrock Gene expression programming firefly algorithm Optimization purpose

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Superior relation-based firefly algorithm for superior solution set search problem

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第12期14卷 1796-1804页

作者： Wang, Hongran Fukushima, Ryu Tamura, Kenichi Tsuchiya, Jun-ichi Yasuda, Keiichiro Tokyo Metropolitan Univ Elect Engn & Comp Sci 1-1 Minamiosawa Hachioji Tokyo 1920397 Japan

In this paper, we analyze the properties of the superior solution set search (SSSS) problem and point out the structural similarities between it and the multiobjective optimization problem. Based on the analyzed properties, we propose a new firefly algorithm (FA) based on superior relations, which can search for the superior solution set by using the user's quantitative desire level as a search strategy. By introducing the distance of the problem space into the moving mechanism, FA makes it possible to search for multiple local optimal solutions by dividing the solution set into multiple groups. By analyzing this property of FA, we clarify the affinity between FA and the SSSS problem. We subsequently analyze the SSSS problem and FA, and discuss their properties together in a similar problem setting. We propose an FA based on superior relations as a new optimization technique for the superior solution set problem based on these analyses. Numerical experiments are then conducted using the SSSS problem and demonstrate the usefulness of the proposed method by comparing the performance of the proposed method with the conventional FA. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： superior solution set search problem metaheuristics firefly algorithm superior relations

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Multipath routing through the firefly algorithm and fuzzy logic in wireless sensor networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2021年第2期14卷 541-558页

作者： Shahbaz, Amir Nader Barati, Hamid Barati, Ali Islamic Azad Univ Dept Comp Engn Dezful Branch Dezful Iran

Routing is one of the major challenges in wireless sensor networks (WSNs). Unbalanced energy consumption in the routing process of data packets is one of the main issues in WSNs. The issue needs consideration, because the energy level of sensor nodes is limited. Multipath routing methods reduce energy consumption, improve scalability and provide load balancing in WSNs. In this study, we suggested a multipath routing method for homogeneous WSNs. The proposed method includes 3 phases: clustering the network nodes, discovering the paths between CHs, and maintaining the paths. In the first phase, wireless sensor network is clustered through the firefly algorithm. In the second phase, routing is performed between CHs based on the fuzzy logic. Routing between CHs results in creating 2 paths: primary path and backup path. CHs transmit data packets to the base station through the primary paths;however, failures in primary paths cause CHs to employ backup paths. In the third phase, the paths are maintained so that path breakages cause to restart route discovery. The results of the simulation reveal that the proposed multipath routing outperforms other routing methods in end-to-end delay, energy consumption, packet loss rate, and network lifetime.

关键词： Wireless sensor networks Multipath routing firefly algorithm Fuzzy logic Scalability

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Optimization of blasting parameters in opencast mine with the help of firefly algorithm and deep neural network

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2022年第3期47卷 1-11页

作者： Bisoyi, Sunil Kumar Pal, Bhatu Kumar Natl Inst Technol Rourkela Dept Min Engn Rourkela 769008 India

Blasting has been one of the most important contributors of mining since the start of mineral extraction and excavation. Along with fragmentation of the rocks, blasting also produces an excess of energy in the form of heat and vibration. Due to the spread of the vibration, the surrounding environment gets affected. Therefore, this paper aims to minimize the vibration to reduce the impact of ground vibration happening due to the mine blasting. In order to optimize the blasting parameters, a good predictor of such vibration is to be created. Hence, the paper compares a lot of predictors including empirical formulas and ANNs (Artificial Neural Networks). The best performing predictor has been used as the objective function for the optimization of parameters. Among the various optimization methods, the firefly algorithm proved to be a very good optimizer. Therefore, it was used to optimize the field parameters and implemented. The resulting optimized parameters showed a significant reduction in the ground vibration of 14.58%.

关键词： Artificial neural networks Back-propagation algorithms Ground vibration Peak particle velocity firefly algorithm Meta-heuristic algorithms

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