CCO

Centered Collision Optimizer: A novel and efficient physics-based metaheuristic optimization algorithm for solving complex real-world engine

This paper presents a novel metaheuristic algorithm, the Centered Collision Optimizer (CCO), inspired by the head-on collision equations in classical physics. CCO employs a unified position update strategy, termed the centered collision strategy, which operates simultaneously in both the original and a decorrelated solution space. This dual-space approach significantly enhances the algorithm's global search capability and improves its ability to escape local optima. Additionally, a Space Allocation Strategy is introduced to accelerate convergence and further improve search efficiency.

Extensive evaluations on three challenging benchmark suites (CEC2017, CEC2019, and CEC2022) across multiple dimensions demonstrate that CCO consistently outperforms 25 high-performance algorithms-including two CEC2017 champion algorithms-in terms of accuracy, stability, and statistical significance. On six constrained engineering design problems and five photovoltaic cell parameter identification tasks, CCO achieves the highest accuracy, consistently ranking first among nine competitive algorithms.

Furthermore, the generalizability of CCO is validated on 33 real-world constrained optimization problems. Even using only a simple external penalty function for constraint handling, CCO attains the best results on 23 problems and achieves a 100\,\% success rate on 21, surpassing three advanced algorithms with specialized constraint-handling mechanisms. These results demonstrate that CCO delivers unprecedented optimization performance across both benchmark tests and practical real-world scenarios.