Handling Constraints in Particle Swarm Optimization Using a Small Population Size

This paper presents a particle swarm optimizer for solving constrained optimization problems which adopts a very small population size (five particles). The proposed approach uses a reinitialization process for preserving diversity, and does not use a penalty function nor it requires feasible solutions in the initial population. The leader selection scheme adopted is based on the distance of a solution to the feasible region. In addition, a mutation operator is incorporated to improve the exploratory capabilities of the algorithm. The approach is tested with a well-know benchmark commonly adopted to validate constrainthandling approaches for evolutionary algorithms. The results show that the proposed algorithm is competitive with respect to state-of-the-art constraint-handling techniques. The number of fitness function evaluations that the proposed approach requires is almost the same (or lower) than the number required by the techniques of the state-of-the-art in the area.

• Type: Conference
• Year: 2007
• Where: MICAI
• Authors: Juan Carlos Fuentes Cabrera, Carlos A. Coello Coello
• Keywords: Artificial Intelligence, Constrained Optimization Problems, MICAI 2007, Particle Swarm Optimizer, Small Population Size