Multiobjective Adaptive Wind Driven Optimization
Zikri Bayraktar, Muge Komurcu
2016
Abstract
In this work, we introduce a new nature-inspired multiobjective numerical optimization algorithm where Pareto dominance is incorporated into Adaptive Wind Driven Optimization for handling multiobjective optimization problems and named as Multiobjective Adaptive Wind Driven Optimization (MO-AWDO) method. This new approach utilizes an external repository of air parcels to record the non-dominated Pareto-fronts found at each iteration via the fast non-dominated sorting algorithm, which are then utilized in the velocity update equation of the AWDO for the next iteration. The performance of the MO-AWDO is tested on five different numerical test functions with two objectives and results indicate that the MO-AWDO offers a very competitive approach compared to well-known methods in the published literature even performing better than NSGA-II for ZDT4 test function.
References
- Deb, K., Pratap A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. In IEEE Transactions on Evolutionary Computation.
- Zitzler, E., Deb, K., Thiele, L., 2000. Comparison of multiobjective evolutionary algorithms: emprical results. Evolutionary Computation.
- Coello, C. A. C., Pulido, G. T., Lechuga M. S., 2004. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation.
- Coello, C. A. C., Lamont, G. B., Van Veldhuizen, D. A., 2007. Evolutionary Algorithm for Solving MultiObjective Problems. Springer, 2nd Edition.
- Bayraktar, Z., Komurcu, M., Werner, D. H., 2010, Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics. IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting.
- Bayraktar, Z., Komurcu, M., 2015. Adaptive wind driven optimization. 9th EAI International Conference on BioInspired Information and Communications Technologies.
- Bayraktar, Z., Komurcu, M., Bossard, J. A., Werner, D. H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE Transactions on Antennas and Propagation.
- Bayraktar, Z., Komurcu, M., Jiang, Z., Werner, D. H., Werner, P. L., 2011. Stub-loaded inverted-F antenna synthesis via wind driven optimization. IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting.
- Bayraktar, Z., Turpin, J. P., Werner, D. H., 2011. Natureinspired optimization of high-impedance metasurfaces with ultra-small interwoven unit cells. IEEE Antennas and Wireless Propagation Letters.
- Deb, K., 2001. Multiobjective Optimization Using Evolutionary Algorithms. Wiley. Chichester U.K.
- Fonseca, C. M., Flemming, P. J., 1993. Genetic algorithm for multiobjective optimization: Formulation, discussion and generalization. Fifth International Conference on Genetic Algorithms.
- Zitzler, E., Thiele, L., 1998. Multiobjective optimization using evolutionary algorithms-A comparative case study. Springer-Verlag, Berlin.
Paper Citation
in Harvard Style
Bayraktar Z. and Komurcu M. (2016). Multiobjective Adaptive Wind Driven Optimization . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 115-120. DOI: 10.5220/0006031801150120
in Bibtex Style
@conference{ecta16,
author={Zikri Bayraktar and Muge Komurcu},
title={Multiobjective Adaptive Wind Driven Optimization},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={115-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006031801150120},
isbn={978-989-758-201-1},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Multiobjective Adaptive Wind Driven Optimization
SN - 978-989-758-201-1
AU - Bayraktar Z.
AU - Komurcu M.
PY - 2016
SP - 115
EP - 120
DO - 10.5220/0006031801150120