A Metaheuristic Approach to Protein Structure PredictionHybrid Metaheuristic Approach for Protein Structure Prediction
A Metaheuristic Approach to Protein Structure Prediction: Hybrid Metaheuristic Approach for...
Jana, Nanda Dulal; Das, Swagatam; Sil, Jaya
2018-03-03 00:00:00
[Hybridization is an integrated framework that combines the merits of algorithms to improve the performance of an optimizer. In this chapter, the synergism of the improved version of particle swarm optimization (PSO) and differential evolution (DE) algorithms are invoked to construct a hybrid algorithm. The proposed method is executed in an interleaved fashion for balancing exploration and exploitation dilemma in the evolution process. The results are tested on ten real protein instances, taken from the protein data bank. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparisons with other hybridization of PSO and DE; and comprehensive learning PSO algorithms.]
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pnghttp://www.deepdyve.com/lp/springer-journals/a-metaheuristic-approach-to-protein-structure-prediction-hybrid-hpkBMrjGHm
A Metaheuristic Approach to Protein Structure PredictionHybrid Metaheuristic Approach for Protein Structure Prediction
[Hybridization is an integrated framework that combines the merits of algorithms to improve the performance of an optimizer. In this chapter, the synergism of the improved version of particle swarm optimization (PSO) and differential evolution (DE) algorithms are invoked to construct a hybrid algorithm. The proposed method is executed in an interleaved fashion for balancing exploration and exploitation dilemma in the evolution process. The results are tested on ten real protein instances, taken from the protein data bank. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparisons with other hybridization of PSO and DE; and comprehensive learning PSO algorithms.]
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