A New Monarch Butterfly Optimization Algorithm with SA Strategy

Abstract

Monarch butterfly optimization (MBO) is a newly proposed meta-heuristic algorithm for solving optimization problems. It has been proved experimentally that MBO is superior to the other five state-of-the-art meta-heuristic algorithms on most test problems. This paper presents a new improved MBO algorithm with Simulated Annealing (SA) strategy, in which the SA strategy is involved into the migration operator and butterfly adjusting operator. So the newly proposed algorithm SAMBO not only accepts all the butterfly individuals whose fitness are better than their parents’ but also randomly selects some individuals that are worse than their parents to disturbance the convergence of algorithm. In the final, the experiments are carried out on 14 famous continuous nonlinear functions; the results demonstrate that SAMBO algorithm is significantly better than the original MBO algorithm on most test functions.