An improved bee colony optimization algorithm with an application to document clustering

Forsati, Rana; Keikha, Andisheh; Shamsfard, Mehrnoush

doi:10.1016/j.neucom.2015.02.048

Cited by 67 publications

(33 citation statements)

References 73 publications

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“…We compared the clustering results of SSODCSC with other clustering algorithms such as K-means, PSO, GA, ABC optimization, ACO, IBCO (Forsati, Keikha & Shamsfard, 2015), SMSSO, BFGSA, SOS, and SSO implementation in which each spider is a collection of K centroids, and found that SSODCSC produces better clustering results.…”

Section: Discussionmentioning

confidence: 99%

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

Thalamala

Janet

Reddy

2019

PeerJ Computer Science

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Nature-inspired algorithms are based on the concepts of self-organization and complex biological systems. They have been designed by researchers and scientists to solve complex problems in various environmental situations by observing how naturally occurring phenomena behave. The introduction of nature-inspired algorithms has led to new branches of study such as neural networks, swarm intelligence, evolutionary computation, and artificial immune systems. Particle swarm optimization (PSO), social spider optimization (SSO), and other nature-inspired algorithms have found some success in solving clustering problems but they may converge to local optima due to the lack of balance between exploration and exploitation. In this paper, we propose a novel implementation of SSO, namely social spider optimization for data clustering using single centroid representation and enhanced mating operation (SSODCSC) in order to improve the balance between exploration and exploitation. In SSODCSC, we implemented each spider as a collection of a centroid and the data instances close to it. We allowed non-dominant male spiders to mate with female spiders by converting them into dominant males. We found that SSODCSC produces better values for the sum of intra-cluster distances, the average CPU time per iteration (in seconds), accuracy, the F-measure, and the average silhouette coefficient as compared with the K-means and other nature-inspired techniques. When the proposed algorithm is compared with other nature-inspired algorithms with respect to Patent corpus datasets, the overall percentage increase in the accuracy is approximately 13%. When it is compared with other nature-inspired algorithms with respect to UCI datasets, the overall percentage increase in the F-measure value is approximately 10%. For completeness, the best K cluster centroids (the best K spiders) returned by SSODCSC were specified. To show the significance of the proposed algorithm, we conducted a one-way ANOVA test on the accuracy values and the F-measure values returned by the clustering algorithms.

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Section: Discussionmentioning

confidence: 99%

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

Thalamala

Janet

Reddy

2019

PeerJ Computer Science

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“…In some literatures, additional information is introduced for text clustering such as side-information [40] and privileged information [41]. What is more, several global optimization algorithms are utilized for text clustering such as particle swarm optimization (PSO) algorithm [42,43] and bee colony optimization (BCO) algorithm [44,45].…”

Section: Clustering Algorithmmentioning

confidence: 99%

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

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Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

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“…It can automatically stop searching for a partial solution with insufficient profitability while the scheduler is creating a new scheduling solution, and consequently save time-cost for the remaining partial solution. Forsati et al (2015) introduce a new bee colony algorithm called improved BCO (IBCO) and applied it to text clustering problem. The algorithm uses two concepts of cloning and fairness to improve exploration and exploitation power of the bees.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Within a generation the algorithm iterates between the second and the fifth stages until all the bees create their full solutions. For further details about BCO interested readers may refer to the work of Forsati et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Weighted bee colony algorithm for discrete optimization problems with application to feature selection

Moayedikia

Jensen

Wiil

et al. 2015

Engineering Applications of Artificial Intelligence

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Document embargo until 20/06/2016The conventional bee colony optimization (BCO) algorithm, one of the recent swarm intelligence (SI) methods, is good at exploration whilst being weak at exploitation. In order to improve the exploitation power of BCO, in this paper we introduce a novel algorithm, dubbed as weighted BCO (wBCO), that allows the bees to search in the solution space deliberately while considering policies to share the attained information about the food sources heuristically. For this purpose, wBCO considers global and local weights for each food source, where the former is the rate of popularity of a given food source in the swarm and the latter is the relevancy of a food source to a category label. To preserve diversity in the population, we embedded new policies in the recruiter selection stage to ensure that uncommitted bees follow the most similar committed ones. Thus, the local food source weighting and recruiter selection strategies make the algorithm suitable for discrete optimization problems. To demonstrate the utility of wBCO, the feature selection (FS) problem is modeled as a discrete optimization task, and has been tackled by the proposed algorithm. The performance of wBCO and its effectiveness in dealing with feature selection problem are empirically evaluated on several standard benchmark optimization functions and datasets and compared to the state-of-the-art methods, exhibiting the superiority of wBCO over the competitor approaches. (C) 2015 Elsevier Ltd. All rights reserved.Peer reviewe

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An improved bee colony optimization algorithm with an application to document clustering

Cited by 67 publications

References 73 publications

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Weighted bee colony algorithm for discrete optimization problems with application to feature selection

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