Abstract
Many science and engineering design problems are modeled as constrained multiobjective optimization problem. The major challenges in solving these problems are (i) conflicting objectives and (ii) non linear constraints. These conflicts are responsible for diverging the solution from true Pareto-front. This paper presents a variation of particle swarm optimization algorithm integrated with accelerated exploration technique that adapts to iteration for solving constrained multiobjective optimization problems. Performance of the proposed algorithm is evaluated on standard constrained multiobjective benchmark functions (CEC 2009) and compared with recently proposed DECMOSA algorithm. The comprehensive experimental results show the effectiveness of the proposed algorithm in terms of generation distance, diversity and convergence metric.
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
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Eberhart, R., Kenedy, J.: Particle swarm optimization. In: Proceedings of IEEE Int. Conference on Neural Networks, Piscataway, NJ, pp. 1114–1121 (November 1995)
Huang, V., Suganthan, P., Liang, J.: Comprehensive Learning Particle Swarm Optimizer for Solving Multi-Objective Optimization Problems. International Journal of Intelligent Systems 21(2), 209–211 (2006)
Sabat, S.L., Ali, L.: The hyperspherical acceleration effect particle swarm optimizer. Appl. Soft. Computing 9(13), 906–917 (2008)
Sabat, S.L., Ali, L., Udgata, S.K.: Adaptive accelerated exploration particle swarm optimizer for global multimodal functions. In: World Congress on Nature and Biologically Inspired Computing, Coimbatore, India, pp. 654–659 (December 2009)
Sarker, R., Abbass, H., Karim, S.: An evolutionary algorithm for constrained multiobjective optimization problems. In: The Fifth Australasia Japan Joint Workshop, pp. 19–21. University of Otago, Dunedin (November 2001)
Zamuda, A., Brest, J., Boškovic, B., Žumer, V.: Differential evolution with self-adaptation and local search for constrained multiobjective optimization. In: CEC 2009: Proceedings of the Eleventh conference on Congress on Evolutionary Computation, pp. 195–202. IEEE Press, Piscataway (2009)
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition. Tech. rep., Nanyang Technological University, Singapore (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ali, L., Sabat, S.L., Udgata, S.K. (2010). Adaptive and Accelerated Exploration Particle Swarm Optimizer (AAEPSO) for Solving Constrained Multiobjective Optimization Problems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-17563-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17562-6
Online ISBN: 978-3-642-17563-3
eBook Packages: Computer ScienceComputer Science (R0)