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A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2018年第24期56卷 7354-7374页

作者： Zhu, Lixia Zhang, Zeqiang Wang, Yi Southwest Jiaotong Univ Sch Mech Engn Chengdu Sichuan Peoples R China Auburn Univ Dept Math & Comp Sci Montgomery AL 36117 USA

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.

关键词： hazard evaluation disassembly line balancing problem firefly algorithm multi-objective optimisation Pareto optimal solution set

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firefly algorithm Using Cluster Information for Superior Solution Set Search

Firefly Algorithm Using Cluster Information for Superior Sol...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, Hongran Tamura, Kenichi Tsuchiya, Junichi Yasuda, Keiichiro Tokyo Metropolitan Univ Dept Elect & Elect Engn 1-1 Minamiosawa Hachioji Tokyo 1920397 Japan

ISBN: (纸本)9781538616451

We analyzed the search characteristics of firefly algorithm (FA), which has a fundamental nature of a Superior Solution Set Search Problem, previously defined in our previous study for single-objective optimization problems. In this study, we proposed a new FA method based on the former problem. This method, which employs cluster information by K-means clustering, is tested for performance by fundamental numerical experiments.

关键词： Metaheuristics Single-Objective Optimization Superior Solution Set Search Problem Cluster K-means clustering firefly algorithm

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firefly algorithm Based Feature Selection for EEG Signal Classification

Firefly Algorithm Based Feature Selection for EEG Signal Cla...

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2020 Medical Technologies Congress (TIPTEKNO)

作者： Ergun, Ebru Aydemir, Onder Recep Tayyip Erdogan Univ Elect & Elect Engn Rize Turkey Karadeniz Tech Univ Elect & Elect Engn Trabzon Turkey

ISBN: (纸本)9781728180731

Bra in computer interfaces (BCIs) recognize specific features of a person's brain signal relating to his/her intent, and output a control command that controls the outside devices or computers. BCI systems facilitate the lives of patients who cannot move any muscles but have no cognitive disorder. The high dimensions of features represent a research challenge. In recent years, especially nature inspired heuristic optimization algorithms became popular in order to eliminate unnecessary features. This paper addresses a crucial factor for effective classification of motor imaginary based EEG signals that are an optimal selection of relevant EEG features using firefly algorithm. firefly algorithm (FA) works on the principle of directing the less shiny than the light intensity emitted by fireflies in nature towards the bright. The algorithm can adaptively select the best subset of features and improve classification accuracy. In this study, following extracted Katz Fractal Dimension based features, effective feature(s) were selected by FA. The proposed method successfully applied on open access dataset which was collected from 29 subjects. We obtained an average 76.14% classification accuracy (CA) using k-nearest neighbor classifier. This is 4.4% higher than the CA calculated by using all features. These results proved that used method is robust for this dataset.

关键词： electroencephalography brain computer interface katz fractal dimension firefly algorithm k-nearest neighbor

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firefly algorithm for congestion management in deregulated environment

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2016年第3期19卷 1254-1265页

作者： Verma, Sumit Mukherjee, V. Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India

In competitive electricity market, congestion is a serious economic and reliability concern. Congestion is a common problem that an independent system operator faces in open access electricity market. This paper presents a reliable and efficient meta-heuristic based approach to solve congestion problem. The proposed approach of the present work employs firefly algorithm (FFA) for alleviation of transmission network congestion in a pool based electricity market via active power rescheduling of generators. FFA is a new meta-heuristic approach based on flashing patterns and behavior of fireflies. Various important security constraints such as load bus voltage and line loading have been taken into account while dealing with congestion problem. The proposed methodology may help in removing the congestion of line with minimum rescheduling cost. The numerical results of modified IEEE 30- and 57-bus test power systems are illustrated. (C) 2016 Karabuk University. Publishing services by Elsevier B.V.

关键词： Congestion management Deregulation firefly algorithm Independent system operator Optimal power flow Price bids

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Opposition-Based firefly algorithm for Earth Slope Stability Evaluation

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China Ocean Engineering 2014年第5期28卷 713-724页

作者： Mohammad KHAJEHZADEH Mohd Raihan TAHA Mahdiyeh ESLAMI Civil Engineering Department Anar Branch Islamic Azad UniversityAnar Iran Civil and Structural Engineering Department National University of MalaysiaBangi Selangor Malaysia Department of Electrical Engineering Kerman Branch Islamic Azad UniversityKerman Iran

This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.

关键词： firefly algorithm opposition based learning safety factor slope stability

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firefly algorithm for instance selection 25

Firefly algorithm for instance selection

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25th KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES)

作者： Czarnowski, Ireneusz Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

The paper focuses on the problem of instance selection. Instance selection is currently crucial to enhance the efficacy and efficiency of machine-learning tools when they are used to solve a data-mining task and when the data are large and they are seen through the prism of the big data phenomenon. Instance selection eliminates redundant instances and thus reduces the size of the training data set. The training data, with redundant cases removed, can be more useful and ensure better performance of the final classification models. The instance selection problem belongs to the NP-hard class, so it can be solved with an approximation tool. In this paper the firefly algorithm is proposed for solving the instance selection problem. This paper is one paper, where the firefly algorithm has been used to solve a discrete optimisation problem, when in more cases previously it has been used for solving continuous optimisation problems. The firefly-based instance selection algorithm is presented and its validation is carried out. The results of the computational experiment show that the algorithm is competitive with others. The results obtained are discussed and conclusions are formulated. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

关键词： instance selection data reduction firefly algorithm machine learning

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firefly algorithm Based Multi-Objective Optimization Using OUPFC in a Power System

Firefly Algorithm Based Multi-Objective Optimization Using O...

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IEEE Region 10 Conference (TENCON)

作者： Balachennaiah, P. Nagendra, P. Annamacharya Inst Technol & Sci Dept EEE Rajampet Andhra Prades India Govt Polytech Dept EEE Uravakonda Andhra Prades India

ISBN: (纸本)9781509011346

firefly algorithm (FA) is employed to optimize the control variables of a hybrid FACTS device known as optimal unified power flow controller (OUPFC) for simultaneous optimization of real power loss (RPL) and voltage stability limit (VSL) of a system. Mathematically, this problem is a constrained multi-objective optimization problem having an objective function of RPL and VSL. OUPFC device is a combination of phase shifting transformer (PST) and a miniature unified power flow controller (UPFC). OUPFC parameters such as OUPFC location, UPFC series injected voltage magnitude and phase angle, and PST phase angle are treated as control variables in the problem formulation. The effectiveness of the proposed FA have been verified on IEEE 14bus and New England 39 bus test systems. The results of the proposed FA with OUPFC device have been compared with the results of the FA with UPFC, as available in the literature. The comparison results show the robustness and efficiency of the method.

关键词： firefly algorithm optimal unified power flow controller real power loss voltage stability limit unified power flow controller phase shifting transformer

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firefly algorithm Scaled Fractional Order Fuzzy PID based PSS for Transient Stability Improvement 19

Firefly Algorithm Scaled Fractional Order Fuzzy PID based PS...

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19th International Carpathian Control Conference (ICCC)

作者： Ray, Prakash K. Paital, Shiba R. Mohanty, Asit Eddy, Foo Y. S. Krishnan, Ashok Gooi, H. B. Amaratunga, G. A. J. Cambridge Ctr Adv Res & Educ Singapore CREATE Tower Singapore Singapore IIIT Bhubaneswar Dept Elect Engn Bhubaneswar India CET Bhubaneswar Dept Elect Engn Bhubaneswar India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Cambridge Elect Engn Div Dept Engn Cambridge CB3 0FA England

ISBN: (纸本)9781538647622

This paper presents an improvement in stability in a single machine connected to infinite bus power system by designing an optimal fractional order fuzzy PID based power system stabilizer (FOFPID-PSS). The low frequency oscillations resulting from load switching are damped out by the designed PSS under different operating conditions. In this paper, a bio-inspired algorithm called firefly algorithm (FA) has been employed for tuning the parameters of the proposed FOFPID-PSS controller. The robustness of the proposed controller is tested for enhancing the transient stability under different operating conditions like step and random variations in load demand. In addition to the graphical results, a comparative analysis of the proposed FOFPID-PSS controller with that of conventional PID-PSS and fuzzy PID-PSS (FPID-PSS) is also presented in terms of the performance indices (PIs) like maximum overshoot, settling time and integral squared error (ISE). The results suggest that the proposed FOFPID-PSS outperforms the FPID-PSS and PID-PSS controllers.

关键词： firefly algorithm FOFPID fuzzy-PID power system stabilizer SMIB transient stability

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A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine

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ISA TRANSACTIONS 2021年 115卷 79-94页

作者： Shan, Jie Pan, Jeng-Shyang Chang, Cheng-Kuo Chu, Shu-Chuan Zheng, Shi-Guang Fujian Univ Technol Sch Elect Engn & Phys Fuchou Fujian Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Flinders Univ S Australia Coll Sci & Engn 1284 South Rd Clovelly Pk SA 5042 Australia Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： firefly algorithm Distributed parallel firefly algorithm Communication strategies Variable pitch control Wind turbine

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Optimization and simulation of micrometre-scale ring resonator modulators based on p-i-n diodes using firefly algorithm

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OPTIK 2017年 128卷 101-112页

作者： Jafari, Omid Akbari, Mahmood Sharif Univ Technol Dept Elect Engn Tehran Iran

Field analysis of ring resonator modulators based on p-i-n diodes has been dissected in this paper. This analysis is performed in time and frequency domains. The conformal transformation method has been used for solving 3-D wave equation. Coupling coefficient between the ring and straight waveguides are obtained by developing the coupled-mode assumption. In the resonant wavelength of 1573.91 nm, a drop of more than 15 dB in frequency spectrum of the device has been observed. Time domain simulation shows that this modulator could support up to 0.4 Gb/s and up to 1.5 Gb/s for NRZ and RZ signals, respectively. Obtained simulation results in both domains have been properly complied with experimental results. Main goal of this paper is to present an optimized model from aforementioned modulator. It will be shown that frequency response could be more optimum than original device by optimizing structure parameters. In order to obtain practical values, firefly algorithm (FA) is used because it finds optimum points locally. In optimized modulator, extinction ratio (ER) could be increased up to 32 dB which is double than original modulator. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Optoelectronics Micro-optical devices Modulators Coupled resonators Guided waves Ring resonator p-i-n diodes firefly algorithm Optimization

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