Chemical reaction optimization for RNA structure prediction

摘要

RNA Structure Prediction (RSP) is an optimization problem, where a stable secondary structure is obtained from an RNA primary sequence. To solve the RSP problem, many exact and metaheuristic algorithms were established in recent years. We have proposed an approach based on metaheuristic algorithm named Chemical Reaction Optimization (CRO) to solve the RSP problem. CRO is a population-based metaheuristic which has been employed in different optimization problems and works better than all other related existing algorithms. We have redesigned the reaction operators of CRO algorithm and calculated the minimum free energy of the RNA structure to solve RSP problem. The operators spread out the population entirely on the solution space using both local and global searches and find the better structure, which makes the proposed algorithm more unique. We have designed a novel operator called Repair function to verify and remove the repeated stem from the solution of an RNA sequence, which makes the process more time efficient. Both the quality of solutions and execution time are considered in designing the basic operators and the repair function. Thus, the proposed methodology gives robustness, efficiency, and effectiveness in solving the problem. The results of the proposed CRO based algorithm for RSP problem are compared with genetic algorithm (RNAPredict), simulated annealing algorithm (SARNA-Predict), coincidence algorithm (COIN), two-level particle swarm optimization algorithm (TL-PSOfold) and Changing Range Bat Algorithm (CRBA) to present that, the proposed work gives better results than those. The significance testing using Kruskal-Wallis test followed by post-hoc analysis also proves that the proposed work outperforms the five related methods.