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Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine

SUSTAINABLE CITIES AND SOCIETY 2017年 35卷 250-256页

作者： Jiang, Minlan Jiang, Lan Jiang, Dingde Xiong, Jiping Shen, Jianguo Ahmed, Syed Hassan Luo, Jingyuan Song, Houbing Zhejiang Normal Univ Dept Elect Informat Technol Jinhua 321004 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Sichuan Peoples R China Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Embry Riddle Aeronaut Univ Dept Elect Comp Software & Syst Engn Daytona Beach FL 32114 USA

The accuracy of dynamic measurement error predicting has the great significant influence on the precision and stability of the sensors. In order to solve the problem of the low accuracy of the model in traditional dynamic measurement error prediction, support vector machine (SVM) is applied to predicting the dynamic measurement error of sensors. However, in prediction tasks, a proper set of design parameters are essential for the performance and efficiency of SVM model. Hence, parameters selection for SVM by firefly algorithm (FA) is proposed in this study to avoid the local minimum value which occurs in the traditional method of parameter optimization. Two sensors of dynamic measurement error data are considered for modeling. Root mean squared error( RMSE) and mean absolute percentage error (MAPE) are employed to evaluate the performances of the models. These results are also compared with the results obtained from the grid search SVM (GS-SVM) and the particle swarm optimization SVM (PSO-SVM). Experimental results show that the model of SVM based on FA algorithm predicts more accurately and effectively on dynamic measurement errors prediction for sensors than the other models.

关键词： Dynamic measurement errors Sensors Errors Support vector machine firefly algorithm Prediction

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Application of firefly algorithm for multi-stage transmission expansion planning with adequacy-security considerations in deregulated environments

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APPLIED SOFT COMPUTING 2016年 41卷 373-389页

作者： Rastgou, Abdollah Moshtagh, Jamal Islamic Azad Univ Kermanshah Branch Dept Elect Engn Kermanshah Iran Univ Kurdistan Dept Elect Engn POB 416 Kurdistan Iran

Electric energy is the most popular form of energy because it can be transported easily at high efficiency and reasonable cost. Nowadays the real-world electric power systems are large-scale and highly complex interconnected transmission systems. The transmission expansion planning (TEP) problem is a large-scale optimization, complicated and nonlinear problem that the number of candidate solutions increases exponentially with system size. Investment cost, reliability (both adequacy and security), and congestion cost are considered in this optimization. To overcome the difficulties in solving the non-convex and mixed integer nature of this optimization problem, this paper offers a firefly algorithm (FA) to solve this problem. In this paper it is shown that FA, like other heuristic optimization algorithms, can solve the problem in a better manner compare with other methods such genetic algorithm (GA), particle swarm optimization (PSO), Simulated Annealing (SA) and Differential Evolution (DE). To show the feasibility of proposed method, applied model has been considered in IEEE 24-Bus, IEEE 118-Bus and Iran 400-KV transmission grid case studies for TEP problem in both adequacy and security modes. The obtained results show the capability of the proposed method. A comprehensive analysis of the GA, PSO, SA and DE with proposed method is also presented. (C) 2016 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Local marginal prices Optimization in power system Transmission expansion planning

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Efficient feedback linearization control for an IPMSM of EVs based on improved firefly algorithm

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ISA TRANSACTIONS 2023年 134卷 431-441页

作者： Sun, Xiaodong Xu, Naixi Yao, Ming Cai, Feng Wu, Minkai Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automobile & Traff Engn Zhenjiang 212013 Peoples R China

The conventional direct torque control (DTC) has high torque and stator flux fluctuation that causes the stator current distortion. This paper presents an efficient control method based on the feedback -linearization direct torque control (FL-DTC) method for an interior permanent magnet synchronous motor (IPMSM) drive by using an improved firefly algorithm. The proposed approach can greatly restrain the poor performance of torque and stator flux. Thus, it is suitable for IPMSM drives in electric vehicles. First, a decoupled linear model is derived to implement the proposed efficient feedback linearization control for the IPMSM. Two phase voltages in d-q axes and two additional control inputs take shape into an isomorphism mapping with the concept of orthogonal transformation. The torque generation is related to the additional control. Second, the Hamiltonian efficient control theory combined with an improved firefly algorithm is applied to obtain an analytical solution. An efficient linearization controller is designed with a cost function considering the maximum voltage of the inverter. Finally, simulation and experiment are carried out to compare the performance of the proposed efficient FL-DTC with the improved firefly algorithm and the conventional direct torque control. The results show that the proposed control method can reduce the torque and flux ripples at a steady state and maintains a good dynamic response with the variations of speed and torque.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Interior permanent magnet synchronous motor (IPMSM) Feedback linearization control Direct torque control firefly algorithm

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Optimised blind image watermarking method based on firefly algorithm in DWT-QR transform domain

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IET IMAGE PROCESSING 2017年第6期11卷 406-415页

作者： Guo, Yong Li, Bing-Zhao Goel, Navdeep Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab MCAACI Beijing 100081 Peoples R China Yadavindra Coll Engn Elect & Commun Engn Sect Punjabi Univ Guru Kashi Campus Talwandi Sabo 151302 Punjab India

firefly algorithm (FA) is one of the newly developed nature inspired optimisation algorithm, inspired by the flashing behaviour of fireflies that a firefly tends to be attracted towards other fireflies with higher brightness. Thus FA has two advantages: local attractions and automatic regrouping. Based on these good properties, a novel image watermarking method based on FA in discrete wavelet transform (DWT)-QR transform domain is proposed in this study. Structural similarity index measure and bit error rate are used in the objective function to trade-off invisibility and robustness. The experiment results show that the proposed image watermarking method not only meet the need of invisibility, but also has better or comparable robustness as compared with some related methods.

关键词： image watermarking optimisation discrete wavelet transforms optimised blind image watermarking method firefly algorithm DWT-QR transform domain FA optimisation algorithm flashing behaviour discrete wavelet transform

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Data Aggregation in Wireless Sensor Networks Using firefly algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2019年第1期104卷 307-324页

作者： Mosavvar, Islam Ghaffari, Ali Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

The challenging issue of data aggregation in wireless sensor networks (WSNs) is of high significance for reducing network overhead and traffic. The majority of transmitted data by sensor nodes is repetitious and doing processes on them in many cases leads to increased power consumption and reduced network lifetime. Hence, sensor nodes should use such a pattern for data transmission which minimizes duplicate data. However, in cluster based WSN, cluster heads (CHs) consume more energy due to aggregating the data from cluster member nodes and transmitting the aggregated data to the sink. Therefore, the proper selection of CHs plays vital role for prolonging the lifetime of WSNs. In WSNs, cluster head selection is an optimization problem which is NP-hard. In this paper, using firefly algorithm, we proposed a method for aggregating data in WSNs. In the proposed method, sensor nodes are divided into several areas by using clustering. In each cluster, nodes are periodically active and inactive. Criteria such as energy and distance are taken into consideration for selecting active nodes. In this way, nodes with more remaining energy and more distance will be selected as active nodes. Simulation results, conducted in MATLAB 2016a, revealed that the proposed method was able to enhance quality of service parameters more than low energy adaptive clustering hierarchy and shuffled frog algorithm methods.

关键词： WSNs Power consumption Data aggregation firefly algorithm Clustering NP-hard

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An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm

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JOURNAL OF SUPERCOMPUTING 2020年第8期76卷 6302-6329页

作者： Kashikolaei, Seyedeh Monireh Ggasemnezhad Hosseinabadi, Ali Asghar Rahmani Saemi, Behzad Shareh, Morteza Babazadeh Sangaiah, Arun Kumar Bian, Gui-Bin Islamic Azad Univ Dept Comp Engn Babol Branch Babol Sar Iran Islamic Azad Univ Ayatollah Amoli Branch Young Researchers & Elite Club Amol Iran Kavosh Inst Higher Educ Comp Dept Mahmood Abad Mazandaran Iran Islamic Azad Univ Dept Comp Sci & Res Branch Tehran Iran Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in complex ways. The major challenge of cloud data centers is to show how the millions of requests of final users are correctly and effectively being investigated and serviced. Load-balancing techniques are needed to increase the flexibility and scalability of cloud data centers. Load-balancing technique is one of the most significant issues in the distributed computing system. Since there are large-scale resources and a lot of user demands in cloud computing load-balancing problem, it could be the main reason that many researchers considered and addressed that as an NP-hard problem. Therefore, some heuristics algorithms such as imperialist competitive algorithm (ICA) and firefly algorithm (FA) had been proposed by previous researchers to solve the mentioned problem. Although ICA and FA could get an approximate satisfying result in solving the cloud computing load-balancing problem, obtaining the better result means to make improvements in makespan, CPU time, load balancing, stability and planning speed. The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result. Local search ability of FA can reinforce ICA algorithm. The obtained result of this research showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed.

关键词： Cloud computing Task scheduling Load balancing Imperialist competitive algorithm firefly algorithm

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

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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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Improved firefly algorithm for feature selection with the ReliefF-based initialization and the weighted voting mechanism

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NEURAL COMPUTING & APPLICATIONS 2023年第1期35卷 275-301页

作者： Yong, Xin Gao, Yue-lin North Minzu Univ Sch Comp Sci & Engn Wenchang North St Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Key Lab Intelligent Informat & Data Wenchang North St Yinchuan 750021 Ningxia Peoples R China

Feature selection has become popular in data mining tasks currently for its ability of improving the performance of the algorithm and gaining more information about the dataset. Although the firefly algorithm is a well-performed heuristic algorithm, there is still much room for improvement as to the feature selection problem. In this research, an improved firefly algorithm designed for feature selection with the ReliefF-based initialization method and the weighted voting mechanism is proposed. First of all, a feature grouping initialization method that combines the results of the ReliefF algorithm and the cosine similarity is designed to take place of random initialization. Then, the direction of the firefly is modified to move toward the optimal solution. Finally, inspired by the ensemble algorithm, a weighted voter is proposed to build recommended positions for fireflies, which is also integrated with the elite crossover operator and the mutation operator to improve the diversity of the population. Selected from the mixed swarm, a new population is constructed to replace the original population in the next stage. To verify the effectiveness of the algorithm proposed in this paper, 18 datasets are utilized and 9 comparison algorithms (e.g., Black Hole algorithm, Grey Wolf Optimizer and Pigeon Inspired Optimizer) from state-of-the-art related works are selected for the simulating experiments. The experimental results demonstrate the superiority of the proposed algorithm applied to the feature selection problem.

关键词： firefly algorithm Feature selection Feature grouping ReliefF algorithm Weighted voting

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