A Hybrid Estimation of Distribution Algorithm with Monarch Butterfly Optimization
Bo Zhu, Li Jia, Jianfang Li · 2024
The complex optimization problems have been investigated deeply by researchers in the optimization community. The estimation of distribution algorithm (EDA) and the monarch butterfly optimization algorithm (MBO) are meta-heuristic algorithms that attracted wide attention. In this study, an improved algorithm based on Estimation of Distribution of Algorithm combined with Monarch Butterfly Optimization Algorithm named EDMBO is proposed. The weighted average of candidate solutions is embedded to estimate the mean value. A linear search strategy is introduced to enhance the exploitation of the algorithm. The CEC 2017 benchmark test suite is adopted to verify the performance of the algorithm. The experimental results show that the EDMBO is competitive.