A new meta-heuristic butterfly-inspired algorithm

Abstract

This paper proposes a novel bio-inspired algorithm named Artificial Butterfly Optimization (ABO) algorithm. The new algorithm is based on the mate-finding strategy of some butterfly species. Two groups of artificial butterflies are employed for simulating the flight strategies. If the flight strategies of artificial butterflies are redefined, ABO can develop a new algorithm. From this point, ABO is a mimic-life algorithm in grandness. By presenting three flight strategies, we build two new algorithms named ABO1 and ABO2. We validate the two new algorithms and compare their performance with other well-known nature-inspired algorithms on twenty-two benchmark functions. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. It also proves that the ABO algorithm provides a new effective computational framework for solving optimization problems.