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International Conference on High Performance Computing and Applications (ICHPCA)

作者： Mathew, Juby Vijayakumar, R. Amaljyothi Coll Engn Dept MCA Kanjirappally Kerala India Mahatma Gandhi Univ Kottayam Kerala India

ISBN: (纸本)9781479959587

This paper mainly focuses in identifying the limitations of the k means algorithm and to propose the parallelization of the k-means using firefly based clustering method. The new parallel architecture can handle large number of clusters. firefly algorithm to find initial optimal cluster centroid and then k-means algorithm with optimized centroid to refined them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. Finally modified algorithm is compared with parallel k means is demonstrated with experiments and it has been found that the performance of modified algorithm is better than the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance

关键词： Clustering k-means parallel k-means firefly algorithm join and fork parallelism

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New progresses in swarm intelligence-based computation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第1期7卷 26-35页

作者： Duan, Haibin Luo, Qinan Beihang Univ BUAA Sch Automat Sci & Elect Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.

关键词： swarm intelligence SI bio-inspired computation ant colony optimisation ACO particle swarm optimisation PSO artificial bee colony ABC firefly algorithm FA bat algorithm BA pigeon inspired optimisation PIO

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Assessing Performance of Genetic and firefly algorithms for Optimal Design of Heat Exchangers

Assessing Performance of Genetic and Firefly Algorithms for ...

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

作者： Khosravi, Rihanna Khosravi, Abbas Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

ISBN: (纸本)9781479938407

This paper aims to comprehensively investigate performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA) and firefly algorithm (FA) are implemented for finding the optimal values for seven key design variables of the STHX model. epsilon-NTU method and Bell-Delaware procedure are used for thermal modelling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA always lead to maximum STHX efficiency. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behaviour of the majority of decision variables remain consistent in different runs of the FA optimization process.

关键词： Heat exchanger optimization genetic algorithm firefly algorithm

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Segregation of MRI brain image using hybrid evolutionary clustering algorithm

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2015年第1期18卷 30-51页

作者： Rajalakshmi, N. Prabha, V. Lakshmi Sanson Engn Ltd Coimbatore Tamil Nadu India Govt Coll Technol Appl Elect Coimbatore 641013 Tamil Nadu India

The present research is on computer-aided classification of the magnetic resonance brain images. The method proposed congregates on colour-converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on Information Gain and Sequential Forward Floating Search (IGSFFS) and Multi-Class Support Vector Machine (MC-SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The present research acknowledges the colour-converted segmentation by new hybrid evolutionary clustering algorithm which is the mixture of weighted firefly and K-means algorithm to overcome local optima problems in firefly algorithm. Further random cluster initialisation is also modelled. The results of the simulation show that the performance of the proposed algorithm has better segmentation accuracy than the other algorithms such as colour-converted PSO-K-means and K-means clustering algorithm. The performance of the method is evaluated using classification accuracy, sensitivity, specificity and Receiver Operating Characteristic (ROC) curves. The results show that the highest classification accuracy of greater than 98% is obtained for the proposed diagnostic model, and this is very promising compared to the previously reported results.

关键词： MRI magnetic resonance imaging biomedical engineering colour-converted K-means clustering algorithm feature selection firefly algorithm MC-SVM

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Bearing Fault Diagnosis Based on IFA-ELM

Bearing Fault Diagnosis Based on IFA-ELM

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2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Qunxian Chen Zekun Zhou Shanghai Dianji University

ISBN: (纸本)9781510835368

Extreme learning machine(ELM) is a simple and effective feedforward neural *** can be used in pattern *** its classification ability is not good *** order to solve this problem,this paper proposed an improved firefly algorithm and used it in the parameters selection of *** establishing the IFA-ELM model,we use UCI standard data set to verify its classification ***,the model is used in bearing fault diagnosis and obtains a good result.

关键词： firefly algorithm extreme learning machine pattern recognition fault diagnosis

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Color Image Segmentation By Cuckoo Search

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2015年第4期21卷 673-685页

作者： Nandy, Sudarshan Yang, Xin-she Sarkar, Partha Pratim Das, Achintya JIS Coll Engn Dept Comp Sci & Engn Nadia 741235 W Bengal India Univ Cambridge Dept Engn Cambridge CB2 1TN England Univ Kalyani DETS Nadia W Bengal India Kolyani Govt Engn Coll ECE Dept Nadia WB India

In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.

关键词： Nature-inspired algorithm firefly algorithm Clustering based image segmentation Genetic algorithm (GA) Cuckoo search Dynamic control particle swarm optimization (DCPSO)

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A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads

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KNOWLEDGE-BASED SYSTEMS 2015年第Mar.期76卷 256-263页

作者： Cheng, Min-Yuan Nhat-Duc Hoang Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei 8862 Taiwan Duy Tan Univ Fac Civil Engn Inst Res & Dev Danang 550000 Vietnam

Due to the disastrous consequences of slope failures, forecasting their occurrences is a practical need of government agencies to develop strategic disaster prevention programs. This research proposes a Swarm-Optimized Fuzzy Instance-based Learning (SOFIL) model for predicting slope collapses. The proposed model utilizes the Fuzzy k-Nearest Neighbor (FKNN) algorithm as an instance-based learning method to predict slope collapse events. Meanwhile, to determine the model's hyper-parameters appropriately, the firefly algorithm (FA) is employed as an optimization technique. Experimental results have pointed out that the newly established SOFIL can outperform other benchmarking algorithms. Therefore, the proposed model is very promising to help decision-makers in coping with the slope collapse prediction problem. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Instance-based learning Swarm intelligence Fuzzy k-Nearest Neighbor Slope collapse prediction firefly algorithm

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Stock Market Prediction Using Clustering with Meta-Heuristic Approaches

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2015年第3期28卷 395-403页

作者： Prasanna, S. Maran, Ezhil Vellore Inst Technol Informat Technol & Engn Vellore Tamil Nadu India Vellore Inst Technol Adv Sci Vellore Tamil Nadu India

Various examinations are performed to predict the stock values, yet not many points at assessing the predictability of the direction of stock index movement. Stock market prediction with data mining method is a standout amongst the most paramount issues to be researched and it is one of the interesting issues of stock market research over several decades. The approach of advanced data mining tools and refined database innovations has empowered specialists to handle the immense measure of data created by the dynamic stock market. Data mining strategies have been utilized to reveal hidden patterns and predict future patterns and practices in financial markets to help financial investors make qualitative choice. In this paper, the consistency of stock index movement of the well-known Indian Stock Market indices NSE-NIFTY are examined with the assistance of famous data mining strategies known as Clustering. Clustering is the methodology of grouping the alike indices into clusters. It likewise audits three of the metaheuristics clustering algorithms: PSO-K-Means, Bat algorithm, and firefly algorithm. These strategies are implemented and tested against a Stock Market Index Movement Dataset. The performance of the aforementioned procedures is compared based on "integrity of clustering" assessment measures. The investigation is used to the NSE-NIFTY and BSE-NIFTY for the period from January 2011 to April 2014.

关键词： Data Mining Clustering PSO BAT algorithm firefly algorithm Prediction Stock Index Movement

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Enhancement of Parallel K-Means algorithm 2

Enhancement of Parallel K-Means Algorithm

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2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Mathew, Juby Vijayakumar, R. Amaljyothi Coll Engn Dept MCA Kanjirappally Kerala India Mahatma Gandhi Univ Kottayam Kerala India

ISBN: (纸本)9781479968183

This paper mainly focuses on identifying the limitations of the K-Means algorithm and to propose the parallelization of the K-Means using firefly based clustering method. The new parallel architecture can handle large number of clusters. Modified firefly algorithm can be used to find initial optimal cluster centroid and then K-Means algorithm with optimized centroid can be used to refine them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. The design methodology is explained in the subsequent sections. Finally, modified algorithm is compared with Parallel K-Means. It is demonstrated with experiments and it has been found that the performance of modified algorithm is better than that of the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques

关键词： Clustering Parallel K -Means firefly algorithm large data

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Fire-fly algorithm for the Optimization of Real Power Loss and Voltage Stability Limit

Fire-fly Algorithm for the Optimization of Real Power Loss a...

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International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)

作者： Balachennaiah, P. Suryakalavathi, M. AITS EEE Dept Ysr Kadapa 516126 AP India JNTU H EEE Dept Hyderabad 500085 Telangana India

ISBN: (纸本)9781479976782

This paper proposes the deployment of a meta-heuristic algorithm known as the firefly algorithm (FA) to find the optimal variables like transformer taps, location of UPFC and its variables in a power system transmission network. All these multi-variables are simultaneously optimized for the optimal power flow (OPF) problem to optimize the real power loss (RPL) as a single objective function and then minimization of RPL and maximization of voltage stability limit (VSL) as multi-objective functions. The proposed algorithm is applied to New England 39 bus test system and the results are compared with Interior point successive linear programming (IPSLP) and Bacteria foraging algorithm (BFA) methods. The results confirm the improvement of RPL and VSL which justifies the efficiency and robustness of the proposed algorithm.

关键词： firefly algorithm RPL VSL IPSLP and BFA

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