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2nd International Conference on Computer Communications Control Technology

作者： Ali, Nadhirah Othman, Mohd Azlishah Misran, Mohamad Harris Nor, Mohd Khanapiah Sulaiman, Hamzah Asyrani Univ Tekn Malaysia Melaka Fac Elect & Comp Engn Ctr Telecommun Res & Innovat CeTRI Durian Tunggal 76100 Melaka Malaysia

ISBN: (纸本)9781479979523

Finding the shortest path or route is a problem that have been studies by years. Many algorithm have been used to solve this problem one of it is firefly algorithm. Thus this paper done some modified for the light absorption coefficient and used the attractiveness matrix as the guidance to choose the next node till all the nodes is visited only once and return back to the beginning.

关键词： firefly algorithm shortest path modification

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firefly algorithm in Biomedical and Health Care: Advances, Issues and Challenges

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SN Computer Science 2020年第6期1卷 1-36页

作者： Nayak, Janmenjoy Naik, Bighnaraj Dinesh, Paidi Vakula, Kanithi Dash, Pandit Byomakesha Department of Computer Science and Engineering Aditya Institute of Technology and Management (AITAM) K Kotturu Andhra Pradesh Tekkali 532201 India Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Engineering Sri Sivani College of Engineering Andhra Pradesh Srikakulam 532402 India

Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Biomedical engineering and healthcare firefly algorithm Metaheuristics Nature-inspired algorithm Swarm intelligence

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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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firefly algorithm to Solve a Project Scheduling Problem 5th

Firefly Algorithm to Solve a Project Scheduling Problem

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5th Computer Science On-line Conference (CSOC)

作者： Crawford, Broderick Soto, Ricardo Johnson, Franklin Valencia, Carlos Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Cient Sur Lima Peru Univ San Sebastian Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cent Chile Santiago Chile Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile

ISBN: (纸本)9783319336251;9783319336237

This paper describes the Software Project Scheduling Problem (SPSP) as a combinatorial optimization problem. In this problem raises the need for a process to assign a set of resources to tasks for a project in a given time, trying to decrease the duration and cost. The workers are the main resource in the project. We present the design of the resolution model to solve the SPSP using an algorithm of fireflies (firefly algorithm, FA). We illustrate the experimental results in order to demonstrate the viability and soundness of our approach.

关键词： firefly algorithm Metaheuristic Software project scheduling problem Project management

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firefly algorithm Enhanced by Orthogonal Learning 7th

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7th Computer Science On-Line Conference (CSOC)

作者： Kadavy, Tomas Pluhacek, Michal Viktorin, Adam Senkerik, Roman Tomas Bata Univ Zlin TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9783319911892

Orthogonal learning strategy, a proven technique, is combined with hybrid optimization metaheuristic, which is based on firefly algorithm and Particle Swarm Optimization. The hybrid algorithm firefly Particle Swarm Optimization is then compared, together with canonical firefly algorithm, with the newly created Orthogonal Learning firefly algorithm. Comparisons have been conducted on five selected basic benchmark functions, and the results have been evaluated for statistical significance using Wilcoxon rank-sum test.

关键词： firefly algorithm Particle Swarm Optimization Orthogonal learning

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firefly algorithm for Reliable Protection in Distribution Networks

Firefly Algorithm for Reliable Protection in Distribution Ne...

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Anand, Rimmi Kumar, Vijay Thapar Univ Comp Sci & Engn Dept Patiala Punjab India

ISBN: (纸本)9781509061068

The aim of this paper is to propose a model for reliable Distribution centers (DCs) in case of unexpected disruption in DCs. Also, random disruptions between links in a distribution network system. The mixed-integer linear programming model (MILP) is formulated that aims to provide reliable DCs in case of random failures. The site-dependent failure probabilities, three investment levels for opening unreliable facility has been considered. The IBM CPLEX 12.6.3 solver has been used to implement recently developed metaheuristic firefly algorithm on the proposed model. Numerical results are presented on basis of random generated examples. The firefly algorithm has outperformed CPLEX on large instances up to 200 customers and 30 DCs.

关键词： Supply chain network design Distribution network Facility location Reliability Disruption firefly algorithm

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firefly algorithm and Grey Wolf Optimizer for Constrained Real-Parameter Optimization

Firefly Algorithm and Grey Wolf Optimizer for Constrained Re...

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Joint World Congress of the International-Fuzzy-Systems-Assoc (IFSA) and the Annual Conference of the North-American-Fuzzy-Information-Proc-Soc (NAFIPS) / 12th International Workshop on Constraint Programming and Decision Making (CoProd)

作者： Rodriguez, Luis Castillo, Oscar Garcia, Mario Soria, Jose Tijuana Inst Technol Tijuana BC Canada

ISBN: (纸本)9783030219208;9783030219192

The main goal of this paper is to present the performance of two popular algorithms, the first is the firefly algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter Optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.

关键词： Grey Wolf Optimizer firefly algorithm Constraints Complex problems Study Optimization

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firefly algorithm for Demand Estimation of Water Resources 24th

Firefly Algorithm for Demand Estimation of Water Resources

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24th International Conference on Neural Information Processing (ICONIP)

作者： Wang, Hui Cui, Zhihua Wang, Wenjun Zhou, Xinyu Zhao, Jia Lv, Li Sun, Hui Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China Nanchang Inst Technol Sch Business Adm Nanchang 330099 Jiangxi Peoples R China Jiangxi Normal Univ Coll Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783319700939;9783319700922

firefly algorithm (FA) is an efficient swarm intelligence optimization technique, which has been used to solve many engineering optimization problems. In this paper, we present a new FA (called NFA) variant for demand estimation of water resources in Nanchang city of China. The performance of the standard FA highly depends on its control parameters. To tackle this issue, a dynamic step factor strategy is proposed. In NFA, the step factor is not fixed and it is dynamically updated during the search process. Three models in different forms (linear, exponential and hybrid) are developed based on the structure of social and economic conditions. Water demand in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 is used for finding the optimal weights, and the rest data (2013-2015) is for testing the models. Simulation results show that three FA variants can achieve promising performance. Our proposed NFA outperforms the standard FA and memetic FA (MFA), and the prediction accuracy is up to 97.91%.

关键词： firefly algorithm Swarm intelligence Water demand estimation Water demand forecasting Optimization

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firefly algorithm for Feature Selection in Sentiment Analysis 3rd

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3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Kumar, Akshi Khorwal, Renu Delhi Technol Univ Dept Comp Sci & Engn Delhi India

ISBN: (纸本)9789811038747;9789811038730

Selecting and extracting feature is a vital step in sentiment analysis. The statistical techniques of feature selection like document frequency thresholding produce sub-optimal feature subset because of the non-polynomial (NP)-hard character of the problem. Swarm intelligence algorithms are used extensively in optimization problems. Swarm optimization renders feature subset selection by improving the classification accuracy and reducing the computational complexity and feature set size. In this work, we propose firefly algorithm for feature subset selection optimization. SVM classifier is used for the classification task. Four different datasets are used for the classification of which two are in Hindi and two in English. The proposed method is compared with feature selection using genetic algorithm. This method, therefore, is successful in optimizing the feature set and improving the performance of the system in terms of accuracy.

关键词： Sentiment analysis Feature selection Swarm intelligence firefly algorithm Genetic algorithm Support vector machine (SVM)

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firefly algorithm for Structural Optimization Using ANSYS 2nd

Firefly Algorithm for Structural Optimization Using ANSYS

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2nd International Conference on Design Tools and Methods in Industrial Engineering (ADM)

作者： Marannano, Giuseppe Ricotta, Vito Univ Palermo Engn Dept Viale Sci I-90128 Palermo Italy

ISBN: (纸本)9783030912345;9783030912338

In the mid-1980s, several metaheuristic methods began to be developed for solving a very large class of computational problems with the aim of obtaining more robust and efficient procedures. Among them, many metaheuristic methods use bio-inspired intelligent algorithms. In recent years, these methods are becoming increasingly important and they can be used in various subject areas for solving complex problems. firefly algorithm is a nature-inspired optimization algorithm proposed by Yang to solve multimodal optimization problems. In particular, the method is inspired by the nature of fireflies to emit a light signal to attract other individuals of this species. In this work, a numerical study for solving a structural problem using the firefly algorithm as optimization method is conducted. In particular, the implementation of the firefly algorithm in several input files realized in the ANSYS Parametric Design Language has allowed the definition of the optimal stacking sequence and the laminate thickness of a composite gear housing used to enclose the components of a mechanical reducer.

关键词： firefly algorithm Structural optimization Finite element analysis ANSYS

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