Structure Optimization by an Improved Tabu Search in the AB Off-Lattice Protein Model
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
3D Protein Structure Prediction with Genetic Tabu Search Algorithm in Off-Lattice AB Model
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 01
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures
Applied Soft Computing
DCABES '10 Proceedings of the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science
Analysis of toy model for protein folding based on particle swarm optimization algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
An Immune Algorithm for Protein Structure Prediction on Lattice Models
IEEE Transactions on Evolutionary Computation
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
| Hi-index | 0.00 |
Predicting the secondary structure of protein has been the focus of scientific research for decades, but it remains to be a challenge in bioinformatics due to the increasing computation complexity. In this paper, AB off-lattice model is introduced to transforms the prediction task into a numerical optimization problem. Artificial Bee Colony algorithm (ABC) is an effective swarm intelligence algorithm, which works well in exploration but poor at exploitation. To improve the convergence performance of ABC, a novel internal feedback strategy based ABC (IF-ABC) is proposed. In this strategy, internal states are fully used in each of the iterations to guide subsequent searching process, and to balance local exploration with global exploitation. We provide the mechanism together with the convergence proof of the modified algorithm. Simulations are conducted on artificial Fibonacci sequences and real sequences in the database of Protein Data Bank (PDB). The analysis implies that IF-ABC is more effective to improve convergence rate than ABC, and can be employed for this specific protein structure prediction issues.