Abstract
It has been shown that swarm topologies influence the behavior of Particle Swarm Optimization (PSO). A large number of connections stimulates exploitation, while a low number of connections stimulates exploration. Furthermore, a topology with four links per particle is known to improve PSO’s performance. In spite of this, there are few studies about the influence of swarm topologies in Multi-Objective Particle Swarm Optimizers (MOPSOs). We analyze the influence of star, tree, lattice, ring and wheel topologies in the performance of the Speed-constrained Multi-objective Particle Swarm Optimizer (SMPSO) when adopting a variety of multi-objective problems, including the well-known ZDT, DTLZ and WFG test suites. Our results indicate that the selection of the proper topology does indeed improve the performance in SMPSO.
The first author acknowledges support from CONACyT and CINVESTAV-IPN to pursue graduate studies in Computer Science. The second author gratefully acknowledges support from CONACyT grant no. 2016-01-1920 (Investigación en Fronteras de la Ciencia 2016) and from a SEP-Cinvestav grant (application no. 4).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications. Advanced Information and Knowledge Processing, pp. 105–145. Springer, London (2005). https://doi.org/10.1007/1-84628-137-7_6
Hardin, D., Saff, E.: Discretizing manifolds via minimum energy points. Not. Am. Math. Soc. 51(10), 1186–1194 (2004)
Huband, S., Barone, L., While, L., Hingston, P.: A scalable multi-objective test problem toolkit. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 280–295. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31880-4_20
Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems. In: 2014 IEEE Symposium on Computational Intelligence in Multi-criteria Decision-Making (MCDM 2014), pp. 170–177, December 2014
Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999), vol. 3, pp. 1931–1938, July 1999
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks (ICNN 1995), vol. 4, pp. 1942–1948 (1995)
Mendes, R.: Population topologies and their influence in particle swarm performance. Ph.D. thesis, Departamento de Informática, Escola de Engenharia, Universidade do Minho, April 2004
Nebro, A.J., Durillo, J.J., García-Nieto, J., Coello, C.A.C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE Symposium on Computational Intelligence in Multi-criteria Decision-Making (MCDM 2009), pp. 66–73, March 2009
Parsopoulos, K.E., Vrahatis, M.N.: Multi-objective particles swarm optimization approaches. In: Multi-objective Optimization in Computational Intelligence: Theory and Practice, pp. 20–42. IGI Global (2008)
Taormina, R., Chau, K.: Neural network river forecasting with multi-objective fully informed particle swarm optimization. J. Hydroinform. 17(1), 99–113 (2014)
Yamamoto, M., Uchitane, T., Hatanaka, T.: An experimental study for multi-objective optimization by particle swarm with graph based archive. In: Proceedings of SICE Annual Conference (SICE 2012), pp. 89–94, August 2012
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. thesis, Swiss Federal Institute of Technology (ETH), Zurich, Suiza, November 1999
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Valencia-Rodríguez, D.C., Coello Coello, C.A. (2020). A Study of Swarm Topologies and Their Influence on the Performance of Multi-Objective Particle Swarm Optimizers. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-58115-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58114-5
Online ISBN: 978-3-030-58115-2
eBook Packages: Computer ScienceComputer Science (R0)