Modern Metaheuristics for Function Optimization Problem

摘要

This paper compares the behaviour of three metaheuristics for the function optimization problem on a set of classical functions handling a lot number of variables and known to be hard. The first algorithm to be described is Particle Swarm Optimization (PSO). The second one is based on the paradigm of Artificial Immune System (AIS). Both algorithms are then compared with a Genetic Algorithm (GA). New insights on how these algorithms behave on a set of difficult objective functions with a lot number of variables are provided.