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RETRACTED: Application of firefly algorithm on optimal power flow control incorporating simplified impedance UPFC model (Retracted article. See vol. 83, pg. 603, 2016)

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 71卷 358-363页

作者： Manoranjitham, Emily G. Alagarsamy, Shunmugalatha SACS MAVMM Engn Coll Madurai Tamil Nadu India Velammal Coll Engn & Technol Madurai Tamil Nadu India

This paper proposes an algorithm to solve optimal power flow (OPF) in power system which has a unified power flow controller (UPFC). This UPFC can improve power transfer capability and transient stability and can reduce the transmission loss and fuel cost of generation. In this paper a new and simplified model of UPFC based on circuit elements for enhancing power flow is presented. Several power flow models of UPFC which are discussed in the literature require complex program codes for computing power injections. Further modification of Jacobian matrix and load flow program structure is a major problem. To reduce these complexities an alternative model of UPFC is proposed and in this model the power injected by the series converter is designed as negative impedance. In order to overcome the nonlinearity of OPE problem it is essential to use a heuristic algorithm. As a result in this paper a firefly algorithm (FFA) to solve the OPF problem is presented. Significantly, firefly algorithm is very efficient in dealing with multimodal global optimization problems. The proposed model is developed with two bus system and the firefly algorithm is tested with standard IEEE 30 bus network using Matlab. The firefly algorithm approach can obtain better solutions than other optimization algorithms. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Component UPFC Negative impedance model Optimal power flow firefly algorithm

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Chaos firefly algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

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IEEE ACCESS 2018年 6卷 45907-45922页

作者： Yang, Yude Wei, Bori Liu, Hui Zhang, Yiyi Zhao, Junhui Manla, Emad Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tec Nanning 530004 Peoples R China Univ New Haven Dept Elect & Comp Engn & Comp Sci West Haven CT 06516 USA

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The effectiveness of the CMFA method to solve economic dispatch problems with high nonlinearities is demonstrated using five classic test power systems. The solutions obtained are compared with the results of the original algorithm and several methods of optimization proposed in the previous literature. The high performance of the CMFA algorithm is demonstrated by its ability to achieve search solution quality and reliability, which reflected in minimum total cost, convergence speed, and consistency.

关键词： Economic dispatch firefly algorithm multiple fuel options valve-point effects

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Improved cervix lesion classification using multi-objective binary firefly algorithm-based feature selection

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2016年第6期8卷 367-378页

作者： Sahoo, Anita Chandra, Satish JSS Acad Tech Educ Dept Comp Sci & Engn Noida 201301 India Jaypee Inst Informat Technol Dept Comp Sci Noida 201301 India

Cervical cancer is one of the vital and most frequent cancers, but can be cured if correctly diagnosed. This work is a novel effort towards developing a methodology for effective characterisation of cervix lesions that may assist radiologists in the diagnostic process by providing a reliable and objective discrimination of benign and malignant lesions in contrast enhanced CT-Scan images. Feature selection, which is a key stage in building such efficient classification models, is NP-hard;where, randomised algorithms do better. Since, firefly algorithm is an efficient biologically inspired randomised algorithm;here it has been utilised for optimal feature selection. This paper presents a multi-objective binary firefly algorithm for wrapper-based feature selection and utilises the selected feature subset for improved classification of cervix lesions. For experiments, contrast enhanced CT-Scan images of 22 patients have been used, where all lesions had been recommended for surgical biopsy by specialists. For characterisation of lesions, grey-level cooccurrence matrix-based texture features are extracted from two-level decomposition of wavelet coefficients. The objective function is designed to minimise the classification error and feature subset length both;making it multi-objective. With 94% accuracy in lesion classification, it has superior performance and greatly reduced execution time than multi-objective genetic algorithm-based feature selection.

关键词： cervix lesion characterisation classification feature selection firefly algorithm multi-objective optimisation textural features wavelet features wrapper-based feature selection bio-inspired algorithm

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A multi-strategy firefly algorithm based on rough data reasoning for power economic dispatch

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第9期19卷 8866-8891页

作者： Zhou, Ning Zhang, Chen Zhang, Songlin Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

Dynamic economic dispatch (DED) is a multi constraint and nonlinear complex problem, which is embodied in the dynamic decision-making coupled with each other in time and space. It is generally transformed into a high-dimensional multi constraint optimization problem. In this paper, a multi Strategy firefly algorithm (MSRFA) is proposed to solve the DED problem. MSRFA puts forward three strategies through the idea of opposite learning strategy and rough data reasoning to optimize the initialization and iteration process of the algorithm, improve the convergence speed of the algorithm in medium and high dimensions, and improve the escape ability when the algorithm falls into local optimization;The performance of MSRFA is tested in the simulation experiment of DED problem. The experimental results show that MSRFA can search the optimal power generation cost and minimum load error in the experiment, which reflects MSRFA superior stability and ability to jump out of local optimization. Therefore, MSRFA is an efficient way to solve the DED problem.

关键词： power system dynamic economic dispatch firefly algorithm opposing learning strategies rough data reasoning relational reasoning

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An efficient technique based on firefly algorithm for pilot design process in OFDM-IDMA systems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2018年第2期26卷 817-829页

作者： Taspinar, Necmi Simsir, Sakir Erciyes Univ Fac Engn Dept Elect & Elect Engn Kayseri Turkey

Accurate placement of pilot tones has been a crucial task in multicarrier transmission technologies since there is a strong relation between pilot positions and channel estimation performance. Therefore, the firefly algorithm (FA) is proposed for achieving the optimal pilot distribution by optimizing the pilot positions in order to minimize the estimation errors of the least squares algorithm employed in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems. According to the simulation results, our proposed FA-based pilot optimizer provides a great performance increase in OFDM-IDMA systems by obtaining the most appropriate pilot distribution pattern among the considered pilot placement strategies. The upper bound of mean square error (MSE) is used as the fitness function in the optimization process for avoiding the matrix inversion operation that is needed when calculating MSE itself.

关键词： OFDM-IDMA pilot tones design channel estimation firefly algorithm

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Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on firefly algorithm and Backpropagation Fuzzy Neural Network

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IEEE ACCESS 2021年 9卷 57020-57032页

作者： Zhang, Hao Zhang, Ruijun He, Qin Liu, Lixin Shandong Jianzhu Univ Sch Mech & Elect Engn Jinan 250101 Peoples R China Shandong Fuji Zhiyu Elevator Co Ltd Dezhou 253400 Peoples R China

To effectively suppress the horizontal vibration of a high-speed elevator car caused by uncertainties such as the irregularity of guide rails and the piston wind in the hoistway, this paper proposes a new variable universe fuzzy control method for semi-active guide shoes with magnetorheological (MR) dampers based on the firefly algorithm and backpropagation (FA-BP) algorithm fuzzy neural network (FNN). First, the dynamic model of a car system is constructed, and the FNN system based non-parametric model of the MR damper is established with the original MR damper data collected from experiments. Then, a variable universe fuzzy main controller is presented to control the input current of the MR damper. As for its variable universe contraction-expansion factors, another FNN auxiliary controller is designed to accurately adjust the universe. Furthermore, the FA-BP algorithm is utilized to train the FNN auxiliary controller. Finally, the elevator experiment is carried out to analyze the characteristics of horizontal vibration, and simulation tests are conducted using the measured excitation signal and two kinds of simulated excitation signals, respectively. The results show that the proposed controller has a lower horizontal vibration acceleration, tilt angle acceleration and other index values than the passive controller or the FNN controller, indicating that the proposed control method can effectively suppress the horizontal vibration of a high-speed elevator. This study can provide the technical foundation for intelligent vibration control of high-speed elevators.

关键词： Fuzzy control Shock absorbers Vibrations Automobiles Fuzzy neural networks Elevators Mathematical model firefly algorithm fuzzy neural network semi-active guide shoe variable universe fuzzy control vibration control

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A novel enhanced exploration firefly algorithm for global continuous optimization problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 5期38卷 4479-4500页

作者： Liu, Jianxun Shi, Jinfei Hao, Fei Dai, Min Zhang, Xiaoya Southeast Univ Sch Mech Engn 2 Southeast Univ Rd Nanjing 211189 Jiangsu Peoples R China Nanjing Inst Technol Sch Mech Engn Nanjing 211167 Peoples R China Guangdong Polytech Ind & Commerce Coll Mech & Elect Engn Guangzhou 510510 Peoples R China

In the global optimization process of the firefly algorithm (FA), there is a need to provide a fast convergence rate and to explore the search space more effectively. Therefore, we conduct modular analysis of the FA and propose a novel enhanced exploration firefly algorithm (EE-FA), which includes an enhanced attractiveness term module and an enhanced random term module. The attractiveness term module can improve the exploration efficiency and accelerate the convergence rate by enhancing the attraction between fireflies. The random term module improves the exploration efficiency by introducing a damped vibration distribution factor. The EE-FA uses multiple parameters to balance its exploration efficiency and convergence rate. The parameters have a great influence on the performance of the EE-FA. In order to achieve the best performance of the EE-FA, each parameter of the EE-FA needs to be simulated to determine its optimal value. Compared to multiple variants of the FA, the EE-FA has better exploration efficiency and a faster convergence speed. Experimental results reveal that the EE-FA recreated consistently vanquishes the front for 24 benchmark functions and 4 real design case studies in terms of both convergence rate and exploration efficiency.

关键词： firefly algorithm Global continuous optimization Convergence speed Damping vibration distribution

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Thermal buckling optimisation of composite plates using firefly algorithm

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2017年第4期29卷 787-794页

作者： Kamarian, S. Shakeri, M. Yas, M. H. Amirkabir Univ Technol Dept Mech Engn Tehran Iran Razi Univ Dept Mech Engn Kermanshah Iran

Composite plates play a very important role in engineering applications, especially in aerospace industry. Thermal buckling of such components is of great importance and must be known to achieve an appropriate design. This paper deals with stacking sequence optimisation of laminated composite plates for maximising the critical buckling temperature using a powerful meta-heuristic algorithm called firefly algorithm (FA) which is based on the flashing behaviour of fireflies. The main objective of present work was to show the ability of FA in optimisation of composite structures. The performance of FA is compared with the results reported in the previous published works using other algorithms which shows the efficiency of FA in stacking sequence optimisation of laminated composite structures.

关键词： Thermal buckling composite plates optimisation firefly algorithm

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Real power performance index and line stability index-based management of contingency using firefly algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第10期10卷 2327-2335页

作者： Mishra, Akanksha Gundavarapu, Venkata Nagesh Kumar Bathina, Venkateswara Rao Duvvada, Deepak Chowdary GITAM Univ GITAM Inst Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India Dr Bullaya Coll Engn Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India

Provision of secured and uninterrupted supply of power is an essential pre-requisite for the electrical industry. Hence, FACTS devices need to be employed efficiently to ensure the security of the power system. This study proposes, a repetition of severity-based strategy of placement of interline power flow controller (IPFC) using composite severity index (CSI) for contingency management. The formulated CSI is a combination of real power performance index) and line stability index L-mn. Thus, the CSI is expected to provide an exact measure of stress on the lines in terms of MW overloading and voltage instability. The IPFC is further tuned for a multi-objective function using firefly algorithm. To verify the proposed methodology, it has been tested and implemented on IEEE 30 and Indian utility 62 bus system. The system has been further tested under highly stressful condition by increasing the load on the maximum loaded bus up to the critical value. The results have been presented and analysed to determine the effectiveness of IPFC for contingency management.

关键词： flexible AC transmission systems power system stability load flow control real power performance index line stability index-based management firefly algorithm severity-based strategy interline power flow controller IPFC composite severity index CSI contingency management voltage instability multiobjective function FACTS devices

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Centroid opposition with a two-point full crossover for the partially attracted firefly algorithm

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SOFT COMPUTING 2019年第23期23卷 12241-12254页

作者： Zhou, Lingyun Ma, Maode Ding, Lixin Tang, Wan South Cent Univ Nationalities Coll Comp Sci Wuhan 430074 Hubei Peoples R China Wuhan Univ Comp Sch Wuhan 430072 Hubei Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

The firefly algorithm (FA) is a powerful optimization tool. However, the existing FA and its variants seldom take advantage of intermediate data generated during algorithm iteration. In this paper, the centroid opposition-based learning with a two-point full crossover (CCOBL) is proposed to make full use of the favor information of the candidate solutions. It adopts a centroid opposition computing for considering the search information of population and a two-point full crossover for using the favor information in the candidate solution and its opposite. Then, the CCOBL is incorporated into the partially attracted firefly algorithm. The proposed algorithm is tested on the CEC' 2013 benchmark suite and a real-world optimization problem and is compared with some state-of-the-art FA algorithms and other up-to-date opposition-based evolutionary algorithms. The experimental results demonstrate the effectiveness of the CCOBL and the better performance of the proposed algorithm.

关键词： firefly algorithm Opposition-based learning Centroid opposition Two-point full crossover

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