Hybridized Monarch Butterfly Algorithm for Global Optimization Problems

Abstract: This paper introduces hybridized monarch butterfly optimization algorithm for solving global optimization problems. Despite of the fact that the monarch butterfly optimization algorithm is relatively new approach, it has already showed great potential when tackling NP-hard optimization tasks. However, by analyzing original monarch butterfly algorithm, we noticed some deficiencies in the butterfly adjusting operator that in early iterations exceedingly directs the search process towards the current best solution. To overcome this deficiency, we incorporated firefly’s algorithm search mechanism into the original monarch optimization approach. We tested our algorithm on six standard global optimization benchamarks, and performed comparative analysis with original monarch butterfly optimization, as well as with other five state-of-the-art metaheuristics. Experimental results are promising.