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...
详细信息
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
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, a...
详细信息
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.
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...
详细信息
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.
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 selecti...
详细信息
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.
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 ...
详细信息
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.
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 processe...
详细信息
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.
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-Optimize...
详细信息
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.
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 stando...
详细信息
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.
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...
详细信息
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
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 transmissi...
详细信息
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.
暂无评论