An Improved Particle Swarm Optimization Algorithm for Protein Structure Prediction Based on Ab Model
نویسندگان
چکیده
An improved particle swarm optimization algorithm, which combines the idea of simulated annealing algorithm and opposition-based learning strategy, is presented for NP-hard protein structure prediction based on AB model. Flying grain is used to control the neighborhood structure of particle, so particle can search the global optimum in solution space more finely. An opposition-based learning is used to keep the diversity of swarm and improve the algorithm’s ability to escape from local optima. Furthermore, the Metropolis criterion of simulated annealing algorithm is used to balance the exploitation and exploration ability. Simulation results show that those strategies can improve the performance of the proposed algorithm effectively.
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