关键词: 大红斑蝶优化算法, 优化, 智能计算

Abstract: Monarch Butterfly Optimization（MBO） is a novel swarm intelligent optimization algorithm. Yet there are still the defects of slow convergence and easy being trapped into local optima in the MBO. In order to overcome the shortcomings of the MBO, an Improved Monarch Butterfly Optimization（IMBO） is proposed in this paper. The IMBO uses the strategy of dynamic and random dividing the population into two sub-populations at every time-step, and the butterflies in different sub-populations usually use different searching methods in order to keep the diversity of population search. Experiments are done on a set of 10 benchmark functions, and the results show that the proposed algorithm has marked advantage of global convergence property, can improve the convergence efficiency in function optimization, and is more stable when being compared with MBO and PSO algorithms.

Key words: Monarch Butterfly Optimization（MBO）, optimization, intelligent computation