A Hybrid Parallel Willow Catkin Optimization Algorithm Applied for Engineering Optimization Problems

The Willow Catkin Optimization Algorithm (WCO) is a newly proposed meta-heuristic algorithm in recent years that has a simple structure and excellent optimization searching ability, but the WCO algorithm could benefit from improvements in both convergence speed and solution diversity. In this paper, the parallel technology is introduced into the WCO algorithm, and by proposing two new communication strategies, the Random Mean (RM) method and the Optimal Flight (OF) method, the goal is to utilize all solution information obtained by each subpopulation in the parallel strategy to enhance the algorithm’s performance. Additionally, the WCO algorithm has been hybridized with the Differential Evolution Algorithm (DE), and a mutation mechanism has been introduced to improve the diversity of solutions. The resulting algorithm is called the Hybrid Parallel Willow Catkin Optimization Algorithm (HPWCO). In this paper, the HPWCO algorithm is tested on the CEC2017 benchmark function set and applied to five real-world engineering optimization problems with constraints, and the experimental results were compared with three types of algorithms: the classical algorithm, the newly proposed algorithm, and the parallel algorithm. The results indicate that the HPWCO performs excellently.