Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization

Metaheuristic optimization algorithms play a crucial role in solving complex real-world problems, including machine learning parameter tuning, yet many existing approaches struggle with maintaining an effective balance between exploration and exploitation, leading to premature convergence and suboptimal solutions. The traditional Parrot Optimizer (PO) is an efficient swarm-based technique; however, it suffers from inadequate adaptability in transitioning between exploration and exploitation, limiting its ability to escape local optima. To address these challenges, this paper introduces the Salp Navigation and Competitive based Parrot Optimizer (SNCPO), a novel hybrid algorithm that integrates Competitive Swarm Optimization (CSO) and the Salp Swarm Algorithm (SSA) into the PO framework. Specifically, SNCPO employs a pairwise competitive learning strategy from CSO, which divides the population into winners and losers. Winners are refined using SSA-inspired salp navigation, enabling enhanced global search in the early stages and a dynamic transition to exploitation. Meanwhile, losers are updated using PO's communication strategy, reinforcing solution diversity and exploration. To validate the efficacy of SNCPO, rigorous experimental evaluations were conducted on CEC2015 and CEC2020 benchmark functions, four engineering design optimization problems, and Extreme Learning Machine (ELM) training tasks across 14 datasets. The results demonstrate that SNCPO consistently outperforms existing state-of-the-art algorithms, achieving superior convergence speed, solution quality, and robustness while effectively avoiding local optima. Notably, SNCPO exhibits strong adaptability to diverse optimization landscapes, reinforcing its potential for real-world engineering and machine learning applications.