A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search

Abstract

There are various mathematical optimization problems that can be effectively solved by metaheuristic algorithms. The advantage of these algorithms is that they perform iterative search processes which efficiently perform exploration and exploitation in the domain space containing local and global optima. In this context, three types of metaheuristic algorithms called firefly algorithm, bat algorithm and cuckoo search algorithm were used to find optimal solutions. Firefly is inspired by behavior of flies, bat algorithm is based on the echolocation behavior of bats while in cuckoo search, a pattern corresponds to a nest and similarly each individual attribute of the pattern corresponds to a cuckoo-egg. A series of computational experiments using each algorithm were conducted. Experimental results were analyzed and it is observed that firefly algorithm seems to perform better than bat algorithm and cuckoo search.