Abstract
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.
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Márquez-Vega, L.A., Torres-Treviño, L.M. (2018). Universal Swarm Optimizer for Multi-objective Functions. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_4
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DOI: https://doi.org/10.1007/978-3-030-04491-6_4
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