Modified Butterfly Optimization Algorithm based on Convergence Factor and Disturbance Strategy
Congwang Hao, Lei Chen, Yunpeng Ma · 2022
The butterfly optimization algorithm (BOA) is a relatively new optimization technology with strong competitiveness compared with other meta-heuristic algorithms. However, BOA has shortcomings in convergence accuracy, convergence speed, and jumping out of local optimum. This paper proposes an improved Butterfly Optimization Algorithm based on convergence factor and disturbance strategy (LCD-BOA) to solve these shortcomings. Based on the BOA algorithm, the Levy flight strategy and disturbance factor strategy are added to improve the exploration ability of the algorithm. In addition, the convergence factor strategy is added to improve the exploitation ability of the algorithm further so that the exploration and exploitation of the algorithm are balanced as far as possible. Finally, the experimental results on 11 benchmark functions prove the effectiveness of the improved algorithm. The experimental results show that the improved algorithm significantly improves the performance of BOA compared with other meta-heuristic algorithms.