A Memory-based Bees Algorithm: An Enhancement

Shatnawi, Nahlah M.; Sahran, Shahnorbanun; Faidzul, Mohammad

doi:10.3923/jas.2013.497.502

“…Many enhancements on basic BA algorithm were proposed such as: neighbourhood shrinking (Ghanbarzadeh, 2007), fuzzy greedy selection based BA algorithm (Pham et al, 2008), proposed BA algorithm (PBA) with pheromone (Packianather et al, 2009), modified bees algorithm by Pham et al (2011), improved BA (IBA) by Ebrahimzadeh et al (2012), BA adaptive neighbourhood enlargement (BA-NE) by Ahmad (2012), bees algorithm using Lévy-flights for start configuration (Shatnawi et al, 2013a), and a memory-based bees algorithm (Shatnawi et al, 2013b), were local memory contains the information acquired from direct interaction with the environment, that used to decide the way of patch visiting based on a waggle dance or depend on it.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Clustering Using Levy Flight and Local Memory Bees Algorithm

Shatnawi

¹

2017

IJBIDM

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Abstract:One of the most popular clustering algorithms is K-means cluster due to its simplicity and efficiency. Although clustering using K-means algorithm is fast and produces good results, it still has a number of limitations including initial centroid selection and local optima. The purpose of this research is to develop a hybrid algorithm that address k-means clustering limitations and improve its performance by finding optimal cluster centre. In this paper, Lévy-flights or Lévy motion is one of non-Gaussian random processes used to solve the initial centroid problem. Bees algorithm is a population-based algorithm which has been proposed to overcome the local optima problem, used along with its local memory to enhance the efficiency of K-means. The proposed algorithm applied to different datasets and compared with K-means and basic Bess algorithm. The results show that the proposed algorithm gives better performance and avoid local optima problem.Keywords: clustering; k-means; Lévy-flights; bees algorithm.Reference to this paper should be made as follows: Shatnawi, N.M. (2017) 'Data clustering using Lévy flight and local memory bees algorithm', Int.

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“…BA algorithm is a population-based and biological inspired algorithm that has been proposed to overcome the local optima problem, used for both combinatorial and functional optimisation problems (Pham et al, 2006a). BA is used to solve many problems like: control chart pattern recognition, job scheduling, data clustering, function optimisation, etc (Shatnawi et al, 2013b).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Shatnawi et al (2013a) BA with Lévy-flights for start configuration proposed to reduce basic BA algorithm tunable parameters. In Shatnawi et al (2013b) memory BA algorithm proposed by adding memory (local and global) to two types of bees to make it more natural, comparing memory-based BA (MBA) with the basic BA show that MBA outperforms basic BA in mean number of evaluations.…”

Section: Introductionmentioning

confidence: 99%

Data clustering using Lévy flight and local memory bees algorithm

Shatnawi

¹

2017

IJBIDM

2

0

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Abstract:One of the most popular clustering algorithms is K-means cluster due to its simplicity and efficiency. Although clustering using K-means algorithm is fast and produces good results, it still has a number of limitations including initial centroid selection and local optima. The purpose of this research is to develop a hybrid algorithm that address k-means clustering limitations and improve its performance by finding optimal cluster centre. In this paper, Lévy-flights or Lévy motion is one of non-Gaussian random processes used to solve the initial centroid problem. Bees algorithm is a population-based algorithm which has been proposed to overcome the local optima problem, used along with its local memory to enhance the efficiency of K-means. The proposed algorithm applied to different datasets and compared with K-means and basic Bess algorithm. The results show that the proposed algorithm gives better performance and avoid local optima problem.Keywords: clustering; k-means; Lévy-flights; bees algorithm.Reference to this paper should be made as follows: Shatnawi, N.M. (2017) 'Data clustering using Lévy flight and local memory bees algorithm', Int.

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“…The resulting improvements in these studies were gained by reducing the number of tunable parameters [12] or by developing tuning methods to tune the parameters of BA [14,15]. Other studies focused on developing other concepts and strategies for the local (neighborhood) search part [16][17][18], or for both the local and global search parts [15,19,20].…”

Section: Introductionmentioning

confidence: 99%