Improved butterfly optimisation algorithm based on guiding weight and population restart
Yanju Guo, Xianjie Liu, Lei Chen
Abstract
Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.