Universal Swarm Optimizer for Multi-objective Functions

摘要

This paper presents the Universal Swarm Optimizer for Multi-Objective Functions (USO), which is inspired in the zone-based model proposed by Couzin that represents in a more realistic way the behavior of biological species as fish schools and bird flocks. The algorithm is validated using 10 multi-objective benchmark problems and a comparison with the Multi-Objective Particle Swarm Optimization (MOPSO) is presented. The obtained results suggest that the proposed algorithm is very competitive and presents interesting characteristics which could be used to solve a wide range of optimization problems.