Abstract: This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications. As a newly mooted meta-heuristic algorithm, snake optimizer (SO) mathematically models the mating characteristics of snakes to find the optimal solution. SO has a simple structure and offers a delicate balance between exploitation and exploration. However, it also has some shortcomings to be improved. The proposed BEESO consequently aims to lighten the issues of lack of population diversity, convergence slowness, and the tendency to be stuck in local optima in SO. The presentation of Bi-Directional Search (BDS) is to approach the global optimal value along the direction guided by the best and the worst individuals, which makes the convergence speed faster. The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics (MEPD), and the replacement of poorer quality individuals improves population quality. The Elite Opposition-Based Learning (EOBL) provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance. The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests. The findings show that these introduced strategies provide some improvements in the performance of SO, and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms. To conclude, the proposed BEESO offers a good alternative to solving optimization issues.

Key words: Snake optimizer , · Bi-Directional Search , · Evolutionary Population Dynamics , · Elite Opposition-Based Learning Strategy , · Mechanical optimization design