Protein Tertiary Structure Prediction Using Artificial Bee Colony Algorithm

Bahamish, Hesham Awadh A.; Abdullah, Rosni; Salam, Rosalina Abdul

doi:10.1109/ams.2009.47

Cited by 37 publications

(14 citation statements)

References 18 publications

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“…Therefore, given the brief literature above, although PSO algorithm is applied to forecast energy demand in Turkey, it is not applied to electricity energy demand until now. In addition, although ABC algorithm and its modifications were successfully used for solving different problems such as clustering [35,64], scheduling [53], designing [34,48,45], vehicle routing [60], prediction [3,31,30]; there is no study which uses ABC algorithm to forecast electricity energy demand in Turkey. Hence, to the best knowledge of the authors, this study is the first study which uses PSO and ABC algorithms to estimate electricity energy demand in Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Kıran

Özceylan

Gündüz

et al. 2012

Knowledge-Based Systems

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Section: Literature Reviewmentioning

confidence: 99%

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Kıran

Özceylan

Gündüz

et al. 2012

Knowledge-Based Systems

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“…ABC was introduced by Karaboga in 2005 [17] and constitutes one of the most prominent approaches in the field of bee-inspired algorithms. The algorithm has been applied to various problem domains including the training of artificial neural networks [19,25], the design of a digital filters [18], solving constrained optimization problems [22], and the prediction of the tertiary structures of proteins [3]. Its optimization performance has been tested and compared to other optimization methods such as Genetic Algorithms (GA), PSO, Particle Swarm Inspired Evolutionary Algorithm (PS-EA), Differential Evolution (DE), and different evolutionary strategies [23,24,20,2].…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms are based on mechanisms underlying the behavior of honeybees and have been successfully applied to various problem domains such as optimization [4], robotics [38], network routing [44], multi-agent systems [31], and protein structure prediction [3].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of artificial bee colony optimization and new selection schemes

et al. 2011

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The artificial bee colony optimization (ABC) is a population-based algorithm for function optimization that is inspired by the foraging behavior of bees. The population consists of two types of artificial bees: employed bees (EBs) which scout for new, good solutions and onlooker bees (OBs) that search in the neighborhood of solutions found by the EBs. In this paper we study in detail the influence of ABC's parameters on its optimization behavior. It is also investigated whether the use of OBs is always advantageous. Moreover, we propose two new variants of ABC which use new methods for the position update of the artificial bees. Extensive empirical tests were performed to compare the new variants with the standard ABC and several other metaheuristics on a set of benchmark functions. Our findings show that the ideal parameter values depend on the hardness of the optimization goal and that the standard values suggested in the literature should be applied with care. Moreover, it is shown that in some situations it is advantageous to use OBs but in others it is not. In addition, a potential problem of the ABC is identified, namely that it performs worse on many functions when the optimum is not located at the center of the search space.Finally it is shown that the new ABC variants improve the algorithm's performance and achieve very good performance in comparison to other metaheuristics under standard as well as hard optimization goals.

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“…First, it was used on numerical benchmark problems, 1,9 on constrained optimization problems in Ref. 10, for training neural networks, 11 Bahamish 2009 used the ABC for protein 3D structure prediction, 12 on fuzzy clustering to classify di®erent data sets, 13 also to solve the TSP problem, 14 and as a feature selection for biomarker Discovery in mass spectrometry analysis. 15 Moreover, authors in Ref.…”

Section: Introductionmentioning

confidence: 99%

A hybrid artificial bee colony algorithm for numerical function optimization

Alqattan

Abdullah

2015

Int. J. Mod. Phys. C

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Arti¯cial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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Protein Tertiary Structure Prediction Using Artificial Bee Colony Algorithm

Cited by 37 publications

References 18 publications

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Performance evaluation of artificial bee colony optimization and new selection schemes

A hybrid artificial bee colony algorithm for numerical function optimization

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