A Systematic Evaluation of Shrike Optimization Algorithm for Multi-Objective multimodal Functions

In this paper, the multi-objective Shrike Optimization Algorithm is analyzed to show the implementation steps and performance. The method primarily draws inspiration from the swarming behaviors of shrike birds in nature. The shrike birds undertake migration from their habitat as a means of ensuring their survival. The optimization process involves two components: exploration and exploitation. The Shrike Optimization Algorithm was thoroughly examined and implemented in both theoretical and practical models. It was specifically applied to a straightforward optimization issue with a basic multi-objective function using a weighted sum strategy for multi-objective with different parameters and performance compared with the Fox optimization algorithm. We concluded that SHOA multi-population, which leads to finding a multi-local solution, will enhance search and convergence rates.