Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction

Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences.