Generation of optimal functions using particle swarm method over discrete intervals

Particle swarm optimization is a computational learning technique designed to find a global and optimal solution upon or within a function. The output, usually singular, is characteristically accurate as the nature of the system is to maintain a balance of convergence and sample diversity. This paper aims to introduce the process of using a multi-level evaluation approach of particle swarm optimization to generate a solution function. Multiple variable assessment is replaced with sequential interval assessment of repeated variables and pieced together to form the framework of an optimized function.