Applying Cooperative Search Strategies to Improve Butterfly Optimization Algorithm

Abstract

Considering that Butterfly Optimization Algorithm (BOA) has low precision, slow convergence speed and is easy to fall into local optimal problems, this paper tries to apply cooperative search strategies for improvement of BOA. Firstly, the Levy flight search strategy is utilized to expand both the global search ability and the local search ability for BOA. Secondly, the differential variation search strategy is introduced in the local search phase, which improves local search ability and helps to jump out of the local optimum. Finally, a dynamic adaptive search strategy is proposed to balance both the global search ability and local search ability, and accelerate the convergence speed of BOA. Simulation results show that, compared with other algorithms, the improved BOA algorithm based on cooperative search strategies accelerates the convergence speed and increases the search accuracy.