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Dynamic clustering with improved binary artificial bee colony algorithm

APPLIED SOFT COMPUTING 2015年 28卷 69-80页

作者： Ozturk, Celal Hancer, Emrah Karaboga, Dervis Erciyes Univ Dept Comp Engn Fac Engn Kayseri Turkey

One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is accepted as one of the simple, novel and efficient binary variant of the artificial bee colony, the applied mechanism for generating new solutions concerning to the information of similarity between the solutions only consider one similarity case i.e. it does not handle all similarity cases. To cover this issue, new solution generation mechanism of the discrete artificial bee colony is enhanced using all similarity cases through the genetically inspired components. Furthermore, the superiority of the proposed algorithm is demonstrated by comparing it with the basic discrete artificial bee colony, binary particle swarm optimization, genetic algorithm in dynamic (automatic) clustering, in which the number of clusters is determined automatically i.e. it does not need to be specified in contrast to the classical techniques. Not only evolutionary computation based algorithms, but also classical approaches such as fuzzy C-means and K-means are employed to put forward the effectiveness of the proposed approach in clustering. The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Automatic clustering Discrete optimization binary artificial bee colony algorithm

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A novel hybrid BPSO-SCA approach for feature selection

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NATURAL COMPUTING 2021年第1期20卷 39-61页

作者： Kumar, Lalit Bharti, Kusum Kumari PDPM Indian Inst Informat Technol Design & Mfg Dumna Airport RdPO Khamaria Jabalpur MP India

Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases.

关键词： binary artificial bee colony algorithm binary particle swarm optimization binary dragonfly algorithm binary moth flame optimization binary whale optimization algorithm Clustering indices Feature selection Sine cosine algorithm

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binary fish swarm algorithm for profit-based unit commitment problem in competitive electricity market with ramp rate constraints

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第13期9卷 1697-1707页

作者： Singhal, Prateek Kumar Naresh, Ram Sharma, Veena Natl Inst Technol Dept Elect Engn Hamirpur Himachal Prades India

This study presents a new approach based on a binary fish swarm algorithm (BFSA) and dynamic economic dispatch (DED) method for generation companies (GENCOs) profit (PF)-based unit commitment (PBUC) problem considering power and reserve generations simultaneously in a day-ahead competitive electricity markets. BFSA is used to decide the units on/off status, whereas the optimum dispatch solution is determined using DED method with modified unit generation limits because of ramp rate constraints over the complete scheduled time horizon. To avoid the search to get trapped at a local optimal solution, swap move-based local search and cyclic re-initialisation operators are embedded in BFSA. Moreover, two strategies for selling power and reserve are considered in problem formulation and implementation phase. Its effectiveness is validated on 10 and 100 thermal units in a day-ahead electricity market in terms of GENCOs PF and computation time. The results obtained for PBUC problem with BFSA method have been compared with those obtained with priority list, dynamic programming, Lagrange relaxation, genetic algorithm and binary artificial bee colony algorithm, and BFSA has been found effective to achieve quality solutions in reasonable computation time.

关键词： BFSA power quality binary artificial bee colony algorithm Thermal power stations and plants search problems binary fish swarm algorithm Other numerical methods DED method Power system management, operation and economics power generation economics thermal power stations Lagrange relaxation Optimisation techniques power supply quality power markets cyclic reinitialisation operator swap move-based local search PF-based PBUC problem profitability genetic algorithm genetic algorithms power generation scheduling day-ahead competitive electricity market GENCO Power supply quality and harmonics dynamic programming power generation dispatch dynamic economic dispatch power selling relaxation theory thermal unit optimum dispatch solution generation company Combinatorial mathematics ramp rate constraints profit-based unit commitment problem

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Feature extraction and I-NB classification of CT images for early lung cancer detection

Feature extraction and I-NB classification of CT images for ...

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International Conference on NanoTechnology - Ideas, Innovation and Industries

作者： Karthiga, B. Rekha, M. Dhanalakshmi Srinivasan Engn Coll Dept Elect & Commun Engn Perambalur India

The tremendous increase in health condition has fixed a novel confront to clinical routine of patients' record about treatment, diagnosis and continuous follow-up with the assistance of data and image processing. It is probable to automate or support the radiologist for the diagnosis of cancer. Identification of lung cancer is performed by MRI, CT scanned image. CT scan shows significance totally and acts as a vast function in earlier diagnosis of cancer. So as to overcome certain shortcoming in selecting the feature and enhance classification, this investigation makes use of a novel evolutionary algorithm termed Accelerated Wrapper based binary artificial bee colony (AWB-ABC) algorithm for efficient feature selection and Improved Naive bayes classifier for classifying the stages of cancer. Here, LUNA 16 dataset is considered as an input dataset, followed by the pre-processing of data for noise removal and image enhancement using MMSE. The morphological features are extracted from the pre-processed image, then the feature or nodule associated with the lung which has the great influence to cause cancer is to be selected, for this purpose, here AWB-ABC algorithm is utilized. At last, an Improved Naive Bayes (I-NBC) algorithm is used for efficient classification. The outcome shows a better trade-off between the prevailing and the proposed techniques. The accuracy of classification is improved by 98% using the proposed method. The simulation was carried out in MATLAB environment. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： LUNA 16 Feature extraction Naive Bayes classifier binary artificial bee colony algorithm Lung cancer CT images

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