Abstract
Optimization algorithms are pivotal in addressing complex problems across diverse domains, including global optimization and feature selection (FS). In this paper, we introduce the Enhanced Crisscross Parrot Optimizer (ECPO), an improved version of the Parrot Optimizer (PO), designed to address these challenges effectively. The ECPO incorporates a sophisticated strategy selection mechanism that allows individuals to retain successful behaviors from prior iterations and shift to alternative strategies in case of update failures. Additionally, the integration of a crisscross (CC) mechanism promotes more effective information exchange among individuals, enhancing the algorithm's exploration capabilities. The proposed algorithm's performance is evaluated through extensive experiments on the CEC2017 benchmark functions, where it is compared with ten other conventional optimization algorithms. Results demonstrate that the ECPO consistently outperforms these algorithms across various fitness landscapes. Furthermore, a binary version of the ECPO is developed and applied to FS problems on ten real-world datasets, demonstrating its ability to achieve competitive error rates with reduced feature subsets. These findings suggest that the ECPO holds promise as an effective approach for both global optimization and feature selection.