A novel human-inspired solution to high-dimensional optimization problems

A novel human-inspired metaheuristic framework, named Sastha Pilgrimage Optimization (SPO), mimics the optimization strategies inherent in the Sabarimala pilgrimage structure and the dynamics of pilgrims’ group behaviors. This methodology utilizes the significant characteristics of the practice, including Guruswamy’s selection (Leader), adaptive group interaction, and trade-offs between exploration and exploitation. It features a leader-based decision mechanism that balances individual performance with group harmony, guided by the most experienced participant. The exploration phase aims to cover a vast search space with dynamic updates in position. In contrast, in the exploitation phase, fine-tuning is achieved with adaptive coefficients and Lévy flights that guarantee convergence to global optima. The mathematical model of the algorithm incorporates new performance and distance threshold values, ensuring that route choices align with both individual and group objectives. A new human-inspired design by SPO can overcome the limitations of nonlinear, high-dimensional optimization challenges when compared to standard methods. The algorithm was benchmarked with over ten complex functions and validated on two datasets: the Cardiovascular dataset for feature selection and classification, and the Brain Tumor MRI dataset for image segmentation. These contributions make SPO a scalable, efficient, versatile optimization tool adaptable to domains requiring precise, high-dimensional decision-making.