| Popis: |
The Snow Ablation Optimizer (SAO) is a powerful metaheuristic algorithm that emulates the process of snow sublimation and melting. Despite exhibiting competitive performance compared to classical algorithms in initial research, SAO still has certain limitations, including suboptimal convergence accuracy, inadequate population diversity, and premature convergence, particularly when faced with high-dimensional complex challenges. To address these limitations, this paper proposes a refined snow ablation optimizer featuring joint opposite selection and multi-strategy fusion (JMRSAO), which integrates three enhancement strategies. Firstly, a good point set opposition-based learning (GPSOL) approach is utilized to generate a population of high-quality individuals that are evenly distributed. This helps the algorithm converge quickly towards the appropriate search space. Next, in both exploration and exploitation phases, we introduce joint opposite selection (JOS) technology to effectively retain superior individuals for future iterations. This promotes information exchange among diverse individuals and establishes a strong balance between exploring new possibilities and exploiting existing knowledge. Furthermore, we incorporate the Powell mechanism (PM) to enhance local exploration and accelerate convergence towards improved solutions. The performance of JMRSAO is evaluated by comparing it with thirteen other metaheuristic algorithms on three benchmark experiments from IEEE CEC2005, IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 as well as six engineering problems. Experimental results demonstrate that JMRSAO effectively addresses the limitations of SAO and exhibits exceptional performance in solving various complex problems while showcasing its potential as an effective problem-solving tool. |
