A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Karaboğa, Derviş; Görkemli, Beyza; Öztürk, Celal; Karaboğa, Nurhan

doi:10.1007/s10462-012-9328-0

Cited by 1,553 publications

(724 citation statements)

References 224 publications

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“…Surveys of the different developments can be found in [103,104,105]. In this paper, we mainly focus on the ABC algorithms that have attracted most of the attention, especially in DOPs [105]. In particular, an ABC algorithm mimics the behaviour of real bees colonies [7].…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

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“…Application areas of the Artificial bee colony is involved in various problem solving approaches such single objective numerical value optimizer, cluster approach in global optimization (26), (27)(28)(29). The abc has been also utilized for the (30,31) and for cluster approach in (31) global optimization (32) participated in (33), (34) the abc is also participated for (35) and participated in various raking problems in wireless sensor networks further it can be witnessed for multi-dimensional problems for both single and multi-objective problems evaluation and differential evolution problems.…”

Section: B Applications Areas Of Ant Colony Optimizationmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.

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“…Count of all bees (employed, onlooker and scout) in swarm is equal to two times of food source. In other words number of employed bees is equal to food source [15].…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…The employed bees whose food sources are evacuated become scouts and search a new solution in search area. In standard ABC, just one employed can become scout in a cycle [15].…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A Pre-Processing Approach Based on Artificial Bee Colony for Classification by Support Vector Machine

Babalik¹,

Babaoğlu²,

Özkış³

2013

IJCCE

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Abstract-In this study, handling with the success of pre-processing on classification tasks, artificial bee colony (ABC) algorithm is used as a pre-processor in order to improve accuracy of the support vector machine (SVM) classifier. Proposed approach is examined on three different online available dataset by using k-fold cross validation method. The results obtained are compared with the results of the classification of the datasets with pure SVM classifier. The increase of the classification accuracy is observed. By altering parameters of the suggested approach, it is thought the approach would be more successful on the different datasets.Index Terms-Artificial bee colony, support vector machine, medical data classification.

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A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Cited by 1,553 publications

References 224 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Review of Bio Inspired Computing and its Applications

A Pre-Processing Approach Based on Artificial Bee Colony for Classification by Support Vector Machine

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