An Improved Monarch Butterfly Optimization with Equal Partition and F/T Mutation

In general, the population of most metaheuristic algorithms is randomly initialized at the start of search. Monarch Butterfly Optimization (MBO) with a randomly initialized population, as a kind of metaheuristic algorithm, is recently proposed by Wang et al. In this paper, a new population initialization strategy is proposed with the aim of improving the performance of MBO. Firstly, the whole search space is equally divided into NP (population size) parts at each dimension. And then, in order to add the diversity of the initialized population, two random distributions (T and F distribution) are used to mutate the equally divided population. Accordingly, five variants of MBOs are proposed with new initialization strategy. By comparing five variants of MBOs with the basic MBO algorithm, the experimental results presented clearly demonstrate five variants of MBOs have much better performance than the basic MBO algorithm.