A Coronavirus Herd Immunity Optimization (CHIO) for Travelling Salesman Problem

In this paper, the travelling salesman problem (TSP) is tackled by coronavirus herd immunity optimizer (CHIO). TSP is the problem of finding the best tour for the salesman in order to visit all cities with minimum cost. In essential, this is a scheduling optimization problem that belongs to NP-hard class in almost all of its variants. CHIO is a recent human-based optimization algorithm that imitated the herd immunity strategy as a way to treat COVID-19 pandemic. The proposed method is evaluated against TSP models of various sizes and complexity including six models (25, 50, 100, 150, 200, and 300) cities. The obtained results are compared against four other methods. They are the genetic algorithm (GA), imperial competitive algorithm (ICA), Keshtel algorithm (KA), and red deer algorithm (RDA). The results prove that the CHIO is able to achieve the best obtained results for all large-scaled problems and produced very comparative results for small TSP problems.