Abstract
To improve the diversity and convergence of multi-objective optimization, a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm using Step-by-step Rejection (SR) strategy is presented in this paper. Instead of using crowding distance based sorting technique, the SR strategy allows only the solution with the least crowding distance to be rejected at one iteration and repeat until the predefined number of solutions selected. With introduction of SR to the selection of particles for next iteration, the modified algorithm MOPSO-SR has shown remarkable performance against a set of well-known benchmark functions (ZDT series). Comparison with the representative multi-objective algorithms, it is indicated that, with SR technique, the proposed algorithm performs well on both convergence and diversity of Pareto solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Mahfouf M, Chen MY, Linkens DA (2004) Adaptive weighted particle swarm optimisation for multi-objective optimal design of alloy steels. Lect Notes Comput Sci 3242:762–771
Chen MY, Zhang CY, Luo CY (2009) Adaptive evolutionary multi-objective particle swarm optimization algorithm. Control Decis 24 (12):1851–1855, 1864
Ping H, Jin-yang Y, Yong-quan Y (2011) Improved niching multi-objective particle swarm optimization algorithm. Comput Eng 37(18):1–3
Zhang L, Xu Y, Wang Z, Li X, Li P (2011) Reactive power optimization for distribution system with distributed generators. Trans China Electrotechn Soc 26(3):168–173
Sierra MR, Coello CAC (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308
Li XD (2003) A non-dominated sorting particle swarm optimizer for multiobjective optimization. Lect Notes Comput Sci 2723:27–48
Mostaghim S, Teich J (2003) Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the 2003 IEEE swarm intelligence symposium, pp 26–33
Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279
Acknowledgments
This work was supported by State Key Laboratory of Power Transmission Equipment & System Security and New Technology (2007DA10512710205) and the National “111” Project (B08036).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, S., Chen, MY., Hu, G. (2013). An Approach for Diversity and Convergence Improvement of Multi-Objective Particle Swarm Optimization. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-37502-6_59
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37501-9
Online ISBN: 978-3-642-37502-6
eBook Packages: EngineeringEngineering (R0)