Socio-Nomadic Learning Optimization

This paper introduces a new hybrid metaheuristic algorithm called Socio-Nomadic Learning Optimization (SNLO), which combines the strengths of two existing algorithms: the Socio Evolution and Learning Optimization Algorithm (SELO) and the Nomadic People Optimizer (NPO). SELO takes inspiration from how families learn socially, helping it explore and make decisions effectively. On the other hand, NPO is based on how nomadic tribes adapt and lead, using a group approach to search large areas for solutions. When tested on standard problems, this algorithm showed faster and better results than other leading methods. The results indicate that SNLO effectively manages to avoid local optima adapting well to various problem landscapes, making it a promising tool for complex optimization tasks.