Optimización de enjambre de partículas para problemas de muchos objetivos

The difficulty to solve problems with many, possibly conflicting, objectives logically increases with the number of objectives, what makes them difficult to solve using multi-objective algorithms like the well known NSGA-II. Therefore, this work proposes the use of a particle swarm optimization (PSO) algorithm to solve many-objective problems. The main premise of this work is that MOPSO may be a good option for solving many-objective problems, presenting experimental evidence that supports this premise using the well known DTLZ benchmark with different performance metrics such as hypervolume, coverage and generational distance, among others.