Abstract
The particle swarm optimization algorithm has been used for solving multi-objective optimization problems in last decade. This algorithm has a capacity of fast convergence; however its exploratory capability needs to be enriched. An alternative method of overcoming this disadvantage is to add mutation operator(s) into particle swarm optimization algorithms. Since the single-point mutation is good at global exploration, in this paper a new coevolutionary algorithm is proposed, which combines single-point mutation and particle swarm optimization together. The two operators are cooperated under the framework of mixed strategy evolutionary algorithms. The proposed algorithm is validated on a benchmark test set, and is compared with classical multi-objective optimization evolutionary algorithms such as NSGA2, SPEA2 and CMOPSO. Simulation results show that the new algorithm does not only guarantee its performance in terms of fast convergence and uniform distribution, but also have the advantages of stability and robustness.
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
Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems (2002)
Coello Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation 3(8), 256–279 (2004)
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)
Hongbin, D., Houkuan, H., Guisheng, Y., Jun, H.: An overview of the research on coevolutionary algorithms. Journal of Computer Research and Development 45(3), 454–463 (2008)
Hongbin, D., Jun, H., Houkuan, H., Wei, H.: Evolutionary programming using a mixed mutation strategy. Information Sciences 1(177), 312–327 (2007)
Mingjun, J., Hualong, Y., Yongzhi, Y., Zhihong, J.: A single component mutation evolutionary programming. Applied Mathematics and Computation 215(10), 3759–3768 (2010)
Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimization. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, pp. 69–73 (1998)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: A History and Analysis (1998)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithm test suites. In: Proc. Symp. Appl. Comput., San Antonio, TX, pp. 351–357 (1999)
Zitzler, E., Laumanns, M., Bleuler, S.: A Tutorial on Evolutionary Multiobjective Optimization (2004)
Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)
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
Zhang, X., Dong, H., Yang, X., He, J. (2012). A Mixed Strategy Multi-Objective Coevolutionary Algorithm Based on Single-Point Mutation and Particle Swarm Optimization. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-31900-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31899-3
Online ISBN: 978-3-642-31900-6
eBook Packages: Computer ScienceComputer Science (R0)