ANT COLONY OPTIMIZATION WITH ITERATED LOCAL SEARCH FOR RNA SEQUENCE DESIGN

Ribonucleic acid (RNA) is a biological molecule that exists in all living organism including plants, animals, bacteria, and viruses. it performs a diversity of functions in the cell such as transferring genetic information, regulating gene expression, and catalyzing many chemical reactions. Recently, artificial design RNA molecules got great interest in biotechnology, medicine, and bioinformatics. In this paper, we propose the ANTRNA-ILS algorithm, an integrated Ant Colony Optimization algorithm (ACO) and Iterated Local Search (ILS) for designing RNA sequences. After applying our model into two well-known benchmark datasets (Rfam and Eterna100), we proved the efficiency of ANTRNA-ILS for designing high quality RNA sequence and increasing the number of successes in a small period of runtime.