A Simple and Efficient Artificial Bee Colony Algorithm

Xu, Yunfeng; Ping, Fan; Yuan, Ling

doi:10.1155/2013/526315

Cited by 69 publications

(58 citation statements)

References 29 publications

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“…The reason is that some data may not supply suitable information for training which lowers the classification rate. The symbiosis based modified DNA-ABC algorithm setting is implemented as follows: The parameter of forage, limit, and the maximum repeat cycle is setting up 100, 100, and 100, which are the same as [36], and apply objective function with DNA computing 10 times. The membership function adjusted by symbiosis based DNA-ABC algorithm, and the consequence is shown in Table 5 and Table 6.…”

Section: Resultsmentioning

confidence: 99%

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.

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Section: Resultsmentioning

confidence: 99%

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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“…Yun Feng Xu et al [14] concentrating a simple and skillful Artificial bee colony algorithm. The employed and onlooker bee phases are modified to a new ABC algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The standard ABC algorithm [24] had disadvantages of hastily falling into local optima and slow convergence rate in later stage [10], [13], and [14]. So that we are using random walk step in this place.…”

Section: Procedurementioning

confidence: 99%

A Method of A-BAT Algorithm Based Query Optimization for Crowd Sourcing System

Cincy¹,

Jeba²

2018

IJISA

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Abstract-In the field of database administration query optimization is one of the refinement processes. In recent years, huge volumes of data are flooded from different resources, which make query optimization, a difficult task for the researchers. In the crowd sourcing, environment query optimization is the biggest problem. The client is simply required to post an SQL-like subject, and the framework assumes the main issue of organizing the inquiry; execution setup is generated and in the crowd sourcing market places the evaluation plan evaluated. In order to retrieve data fast and reduce query processing time, Query optimization is badly required. In order to optimize the queries, Meta heuristic techniques are used. In this proposed paper, preprocessing method is used to mine the information from the Crowd. The Original population based ABC algorithm has low convergence speed. In this paper a novel A-BAT algorithm is proposed, which highly improve convergence speed, accuracy and Latency. This algorithm uses a Random walk phase. The proposed algorithm had better optimization accuracy, convergence rate, and robustness.

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“…The hybrid crossover based ABC (CBABC) [25] is an exploitative variant that strengthens the exploitation phase of ABC by using a crossover operation. Another recent ABC-variant -NABC [26] alters the search pattern of employed and onlooker bees by searching around neighborhood of best solutions. JA-ABC [27] tries to improve average fitness of bee population by replacing poor solutions with perturbations of the fittest solution, which makes it exploitative.…”

Section: Existing Variants Of the Abc Algorithmmentioning

confidence: 99%

“…The explorative enhancements are usually based on more explorative perturbation, selection and/or initialization (e.g., [18], [19]) or employing some technique to maintain more population diversity (e.g., [16], [17]), while the exploitative developments are usually based on increasing the local search operations around the best candidate solutions ( [21] - [22], [26] - [27]). Another limitation of all these ABC-variants (e.g., [16] - [34]) is that they do not consider the individual explorative/exploitative needs of the candidate solutions; rather they treat all the candidate solutions equally, employing some population-wide uniform strategy, identically on all candidate solutions.…”

Section: Existing Variants Of the Abc Algorithmmentioning

confidence: 99%

On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization

Ahmed¹,

Shahriar²,

Shafiul³

2015

IJAIS

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The Explorative Artificial Bee Colony (EABC) algorithm is a recently introduced swarm intelligence based algorithm that has been successfully tested to optimize only a limited number of multimodal functions. This paper evaluates EABC on a larger number of benchmark functions, including both unimodal and multimodal functions. EABC is an improved variant of the Artificial Bee Colony (ABC) algorithm. A major problem with the basic ABC algorithm is that it is more aligned towards exploitations, rather than explorations, which often leads to premature convergence and fitness stagnation. The improved variant -EABC tries to increase the degree of explorations of ABC by introducing more randomness during its perturbation operations. Besides, EABC customizes the degree of exploitations and explorations at the individual solution level, separately for each candidate solution of the bee population. EABC also introduces a crossover operation that assists the explorative perturbation operation of EABC. This paper extends the experimental studies on EABC by evaluating it on as many as 13 complex, high dimensional benchmark functions, including both unimodal and multimodal, separable and non-separable functions. The results are compared with the basic ABC algorithm. The comparison demonstrates that EABC often performs better optimization than the original ABC algorithm, which indicates the effectiveness of its more explorative operations.

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A Simple and Efficient Artificial Bee Colony Algorithm

Cited by 69 publications

References 29 publications

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

A Method of A-BAT Algorithm Based Query Optimization for Crowd Sourcing System

On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization

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