A novel clustering approach: Artificial Bee Colony (ABC) algorithm

Karaboğa, Derviş; Öztürk, Celal

doi:10.1016/j.asoc.2009.12.025

Cited by 948 publications

(434 citation statements)

References 26 publications

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“…The position of a food source represents a possible solution for the problem under consideration, and the amount of nectar in the food source represents the quality of the solution based on its 'fitness' value [22,23]. In the minimization problem, the fitness can be computed by the objective function.…”

Section: Artificial Bee Colony (Abc) Algorithmmentioning

confidence: 99%

Reliability-based Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses

Zhao

Zhang

Zhu

2017

Period. Polytech. Civil Eng.

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IntroductionThe design and performance of tunnels are usually affected by some uncertainties that can be costly and time-consuming for tunnel construction projects. Traditional empirical and deterministic design approaches do not include uncertainty in tunnel support design [1][2][3], but tend to be based on trial-anderror processes that consider safety and cost [4][5][6]. Reliabilitybased optimization (RBO) makes provision for the uncertainty of structures by adding probabilistic constraints. This is quite straight forward if the results of the reliability analysis are accurate and precise so that no question arises as to whether a given design satisfies safety requirements. The purpose of RBO is to find a balanced design that is not only economical but also reliable in the presence of uncertainty [7].Over the past few decades, numerous reliability optimization techniques have been proposed [6,8,9]. Younes and Alaa overviewed the various RBDO approaches using mathematical and finite element models with different levels of difficulties [10]. Marcos and Gerhart (2010) produced a detailed literature review on reliability-based optimization [11]. Although RBO has some evident advantages overdeterministic optimization design, it is often computationally inefficient. Response surface methodology has been applied in RBO in attempts to improve its efficiency [12,13]. Zhang et al. applied the mean first-order reliability method (MFORM) to the optimization of geotechnical systems [6]. Those methods improved the computational efficiency but decreased the accuracy of the reliability analysis, which affects the results of RBO. The selection of an optimization method is critical to RBO applications, especially for complex nonlinear optimization problems. Gen and Yun reviewed the application of soft computing methods in reliability optimization [14]. Genetic algorithms and particle swarm optimization have also been applied to RBO [7,15]. Lee et al. proposed a methodology to convert an RBDO problem requiring very high reliability to an RBDO problem requiring relatively low reliability by appropriately increasing the input standard deviations for efficient computation in sampling-based RBDO [16].

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Section: Artificial Bee Colony (Abc) Algorithmmentioning

confidence: 99%

Reliability-based Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses

Zhao

Zhang

Zhu

2017

Period. Polytech. Civil Eng.

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“…As in classification task in data mining, ABC algorithm also provide a good performance in gathering data into classes [33]. Hence motivated by these studies, the ABC algorithm is utilized in this work as an optimization tool to optimize FLNN learning for a prediction task.…”

Section: Artificial Bee Colony Optimizationmentioning

confidence: 99%

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

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Abstract. Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

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“…Nevertheless, K-means always converge into local optima. Such a situation has led researchers to the K-means with Swarm intelligent algorithms in order to search for optimal solution (Cui et al, 2005;He et al, 2006;Karaboga and Ozturk, 2011;Zaw and Mon, 2013). The pseudo code of K-means (Jain, 2010) shows in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Swarm Intelligent is defined as "The emergent collective intelligence of groups of simple agents" (Bonabeau et al, 1999). Examples of Swarm Intelligent algorithms includes the Artificial Bee Colony (ABC) (Karaboga and Ozturk, 2011), Cuckoo Search Optimization algorithm (Zaw and Mon, 2013), Ant Colony Optimization (He et al, 2006) and particle swarm optimization (Cui et al, 2005). These types of Swarm Intelligent algorithms have been utilized in text clustering; however, they need to predefine the number of k clusters.…”

Section: Introductionmentioning

confidence: 99%

Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization. This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering. We present two variants of FA; Weight-based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFA II ). The difference between the two algorithms is that the WFA II, includes a more restricted condition in determining members of a cluster. The proposed FA methods are later evaluated using the 20Newsgroups dataset. Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFA II outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved.

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A novel clustering approach: Artificial Bee Colony (ABC) algorithm

Cited by 948 publications

References 26 publications

Reliability-based Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses

Reliability-based Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Document Clustering Based on Firefly Algorithm

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