Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model

Li, Bai; Wang, Yongdong; Gong, Ligang

doi:10.1016/j.engappai.2013.06.010

Cited by 76 publications

(29 citation statements)

References 32 publications

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“…The proposed APM-BA algorithm is compared with simple bees algorithm [20] and the algorithms which are already used for protein structure prediction such as CPSO [10], EPSO [11], IF-ABC [18] in 2D off-lattice model. All experiments are implemented in MATLAB R2010a and executed on an Intel Core (TM) 17-2670 QMCPU running at 2.20 GHz with 8 GB of RAM with Windows XP.…”

Section: Parameter Settings and Initializationmentioning

confidence: 99%

“…An offlattice model is a generalization of HP lattice model known as AB off-lattice model proposed by Stillinger et al [8] where the hydrophobic and the hydrophilic residues are labelled by 'A' and 'B' respectively. Many computational intelligent algorithms have been applied on AB off-lattice model for finding global minimum energy to predict protein structure [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Jana

Sil

Das

2015

Advances in Intelligent Systems and Computing

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Protein Structure Prediction (PSP) using sequence of amino acids is a multimodal optimization problem and belongs to NP hard class. Researchers and scientists put their efforts to design efficient computational intelligent algorithm for solving this kind of problem. Bees Algorithm (BA) is a swarm intelligence based algorithm inspired by the foraging behaviour of honey bees colony, already exhibits its potential ability for solving optimization problems. However, it may produce premature convergence when solving PSP like problems. To prevent this situation, Adaptive Polynomial Mutation based Bees Algorithm (APM-BA) has been proposed in this paper for predicting protein structure in 2D AB off-lattice model. In this strategy, each of best scout bees are mutated with adaptive polynomial mutation technique when their performances are no more improve during execution phase. The experiments are conducted on artificial and real protein sequences and numerical results show that the proposed algorithm has strong ability for solving PSP problem having minimum energy.

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Section: Parameter Settings and Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Jana

Sil

Das

2015

Advances in Intelligent Systems and Computing

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“…BCO is one of the most recent developments of swarm intelligence proposed by Teodorovic et al (2006), which has been successfully applied to many fields of science including image analysis (Ghareh Mohammadi and Saniee Abadeh, 2014), bioinformatics (Li et al, 2014), etc. The algorithm simulates the natural behavior of the bees in locating food resources.…”

Section: Introductionmentioning

confidence: 99%

Weighted bee colony algorithm for discrete optimization problems with application to feature selection

Moayedikia

Jensen

Wiil

et al. 2015

Engineering Applications of Artificial Intelligence

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Document embargo until 20/06/2016The conventional bee colony optimization (BCO) algorithm, one of the recent swarm intelligence (SI) methods, is good at exploration whilst being weak at exploitation. In order to improve the exploitation power of BCO, in this paper we introduce a novel algorithm, dubbed as weighted BCO (wBCO), that allows the bees to search in the solution space deliberately while considering policies to share the attained information about the food sources heuristically. For this purpose, wBCO considers global and local weights for each food source, where the former is the rate of popularity of a given food source in the swarm and the latter is the relevancy of a food source to a category label. To preserve diversity in the population, we embedded new policies in the recruiter selection stage to ensure that uncommitted bees follow the most similar committed ones. Thus, the local food source weighting and recruiter selection strategies make the algorithm suitable for discrete optimization problems. To demonstrate the utility of wBCO, the feature selection (FS) problem is modeled as a discrete optimization task, and has been tackled by the proposed algorithm. The performance of wBCO and its effectiveness in dealing with feature selection problem are empirically evaluated on several standard benchmark optimization functions and datasets and compared to the state-of-the-art methods, exhibiting the superiority of wBCO over the competitor approaches. (C) 2015 Elsevier Ltd. All rights reserved.Peer reviewe

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“…While the third focuses on changing the framework of the ABC algorithm. An incomplete collection of references [28,[35][36][37][38][39][40][41][42][43][44][45][46][47][48] is used in Figure 1 to provide a quick overview of possible modifications made to the conventional ABC algorithm. In this work, we adopt the ABC algorithm to solve the transformed numerical optimization problem of search-evasion path planning for invading submarines.…”

Section: Introductionmentioning

confidence: 99%

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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Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model

Cited by 76 publications

References 32 publications

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Weighted bee colony algorithm for discrete optimization problems with application to feature selection

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

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