Abstract
In this study, we present a novel particle swarm optimizer, called Gender-Hierarchy Based Particle Swarm Optimizer (GH-PSO), to handle multi-objective optimization problems. By employing the concepts of gender and hierarchy to particles, both the exploration ability and the exploitation skill are extended. In order to maintain an uniform distribution of non-dominated solutions, a novel proposal, called Rectilinear Distance based Selection and Replacement (RDSR), is also proposed. The proposed algorithm is validated by using several benchmark functions and metrics. The results show that the proposed algorithm outperforms over MOPSO, NSGA-II and PAES-II.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Moore, J., Chapman, R., Dozier, G.: Multiobjective Particle Swarm Optimization. In: ACM-SE 38: Proceedings of the 38th Annual on Southeast Regional Conference, pp. 56–57 (2000)
Coello Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)
Moore, J., Chapman, R.: Application of Particle Swarm to Multiobjective Optimization. Dept. of Computer Science Software Engineering, Auburn University (1999)
Ray, T., Liew, K.M.: A swarm metaphor for multiobjective design optimization. Engineering Optimization 34(2), 141–153 (2002)
Hu, X., Eberhart, R.: Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1677–1681 (2002)
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)
Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing, pp. 603–607 (2002)
Gao, J., Li, H., Hu, L.: Gender-Hierarchy Particle Swarm Optimizer Based on Punishment. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 94–101. Springer, Heidelberg (2010)
Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Ph.D. thesis, Vanderbilt University, Nashville, Tennessee (1984)
Kursawe, F.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)
Knowles, J.D., Corne, D.W.: Approximating the Non-dominated Front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wei, W., Zhang, W., Jiang, Y., Li, H. (2012). Handling Multi-optimization with Gender-Hierarchy Based Particle Swarm Optimizer. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-30976-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30975-5
Online ISBN: 978-3-642-30976-2
eBook Packages: Computer ScienceComputer Science (R0)