A Comparative Study of Artificial Bee Colony, Bees Algorithms and Differential Evolution on Numerical Benchmark Problems

Li, Huanzhe; Liu, Kunqi; Li, Xia

doi:10.1007/978-3-642-16388-3_22

Cited by 21 publications

(15 citation statements)

References 4 publications

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“…The employed bee whose food source has been exhausted by other bees becomes a scout. First, the scouts are sent to the initial food sources and then a procedure is performed iteratively until the requirements are met (Li, Liu, & Li, 2010). ABC algorithm has been implemented for optimizing various multivariable functions and the results compared with genetic algorithm and particle swarm optimisation have shown that ABC outperforms the other approaches (Karaboga & Basturk, 2007;Li, Liu, & Li 2010).…”

Section: Different Honey Bee Foraging Inspired Algorithmsmentioning

confidence: 99%

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

PourkamaliAnaraki

Sadeghi

2015

Journal of Experimental & Theoretical Artificial Intelligen

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Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link.

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Section: Different Honey Bee Foraging Inspired Algorithmsmentioning

confidence: 99%

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

PourkamaliAnaraki

Sadeghi

2015

Journal of Experimental & Theoretical Artificial Intelligen

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“…Various studies using different numerical benchmark tests have confirmed that the ABC algorithm possesses competitive advantages compared to some other swarm intelligence and evolutionary algorithms [30][31][32][33] (in regard to the question of what is an "evolutionary algorithm", see [34] for some interesting discussion). In addition, the algorithm framework of ABC is relatively simple, thus making it possible to acquire good results at a low computational cost [35].…”

Section: Introductionmentioning

confidence: 96%

Search-evasion path planning for submarines using the Artificial Bee Colony algorithm

Chiong

Gong

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.

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“…Its relatively small number of control parameters makes ABC flexible and easy to execute for novice users (Li et al 2011). Researchers have demonstrated that ABC is superior to other algorithms in identifying optimal solutions (Karaboga and Akay 2009;Kıran et al 2012;Li et al 2010;Şencan et al 2011). ABC is also a reliable tool when paired with other data mining techniques (Hong 2011).…”

Section: Introductionmentioning

confidence: 98%

Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance

2015

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This study aims to build an artificial intelligence (AI)-based inference model to predict the coefficient of performance (COP) for refrigeration equipment under various R404A refrigerant conditions. The proposed model, the evolutionary multivariate adaptive regression splines (EMARS), is a hybrid of the multivariate adaptive regression splines (MARS) and the artificial bee colony (ABC). In the EMARS, the MARS primarily addresses the learning and curve fitting and the ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. A tenfold cross-validation method was used to compare the performance of the EMARS against four other AI techniques, including the back-propagation neural network, classification and regression tree, genetic programming, and support vector machine. An analysis of comparison results supports EMARS as the best model for predicting the COP, with an MAPE value <1%. In addition to performing significantly better than the four benchmark models, EMARS is unique in being able to: operate automatically without human intervention or domain knowledge; explore the approximate function of the input-output relationship; and identify the relative importance of various factors of influence automatically.

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A Comparative Study of Artificial Bee Colony, Bees Algorithms and Differential Evolution on Numerical Benchmark Problems

Cited by 21 publications

References 4 publications

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

Search-evasion path planning for submarines using the Artificial Bee Colony algorithm

Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance

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