Abstract
The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm towards the Pareto optimal front. The proposed algorithm uses a Differential Evolution operator to create the leaders. These leaders can successfully guide the other particles towards the Pareto optimal front for various types of test problems. This simple yet robust algorithm is effective compared with existing multi-objective particle swarm algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Reyes-Sierra, M., Coello Coello, C.A.: Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research 2, 287–308 (2006)
Kennedy, J., Eberhart, R.C.: Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 37–48. Springer, Heidelberg (2003)
Li, X.: Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 117–128. Springer, Heidelberg (2004)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Secaucus (2005)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Abbass, H., Sarker, R., Newton, C.: PDE: a pareto-frontier differential evolution approach for multi-objective optimization problems. IEEE Congress on Evolutionary Computation (CEC) 2, 971–978 (2001)
Huang, V.L., Suganthan, P.N., Qin, A.K., Baskar, S.: Multiobjective differential evolution with external archive and harmonic distance-based diversity measure. In: School of Electrical and Electronic Engineering Nanyang, Technological University Technical Report (2005)
Kukkonen, S., Lampinen, J.: GDE3: the third evolution step of generalized differential evolution. In: IEEE Congress on Evolutionary Computation (CEC), pp. 443–450 (2005)
Robic, T., Filipic, B.: Demo: Differential evolution for multiobjective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)
Hu, X., Eberhart, R., Shi, Y.: Particle swarm with extended memory for multiobjective optimization. In: IEEE Swarm Intelligence Symposium (SIS), pp. 193–197 (2003)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Balling, R.: The maximin fitness function; multi-objective city and regional planning. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 1–15. Springer, Heidelberg (2003)
Fieldsend, J., Everson, R., Singh, S.: Using unconstrained elite archives for multiobjective optimization. IEEE Transactions on Evolutionary Computation 7, 305–323 (2003)
Reyes-Sierra, M., Coello Coello, C.A.: Improving pso-based multi-objective optimization using crowding, mutation and epsilon-dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)
Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization. In: IEEE Swarm Intelligence Symposium (SIS), pp. 26–33 (2003)
Allmendinger, R.: Reference point-based particle swarm optimization using a steady-state approach: Master’s thesis (2008)
Hendtlass, T.: A combined swarm differential evolution algorithm for optimization problems. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS, vol. 2070, pp. 11–18. Springer, Heidelberg (2001)
Zhang, W.J., Xie, X.F.: DEPSO: Hybrid particle swarm with differential evolution operator. In: IEEE International Conference on Machine Learning and Cybernetics (ICMLC), pp. 3816–3821 (2003)
Hao, Z.F., Guo, G.H., Huang, H.: A particle swarm optimization algorithm with differential evolution. IEEE International Conference on Machine Learning and Cybernetics (ICMLC) 2, 1031–1035 (2007)
Xu, X., Li, Y., Fang, S., Wu, Y., Wang, F.: A novel differential evolution scheme combined with particle swarm intelligence. In: IEEE Congress on Evolutionary Computation, CEC (2008)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 173–195 (2000)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Evolutionary Multiobjective Optimization (EMO): Theoretical Advances and Applications, pp. 105–145. Springer, Heidelberg (2005)
Huband, S., Hingston, P., Barone, L., While, R.L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10, 477–506 (2006)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wickramasinghe, U., Li, X. (2008). Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-89694-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89693-7
Online ISBN: 978-3-540-89694-4
eBook Packages: Computer ScienceComputer Science (R0)