IMPROVED COMPRESSED OBJECTIVE GENETIC ALGORITHM: COGA-II
Kittipong Boonlong, Nachol Chaiyaratana, Kuntinee Maneeratana
2010
Abstract
This paper presents an improved version of compressed objective genetic algorithm to solve problems with a large number of objectives. The improved compressed objective genetic algorithm (COGA-II) employs a rank assignment for the screening of non-dominated solutions that best approximate the Pareto front from vast numbers of available non-dominated solutions. Since the winning non-dominated solutions are heuristically determined from the survival competition, the procedure is referred to as a winning-score based ranking mechanism. In COGA-II, an m-objective vector is transformed to only one criterion, the winning score of which assignment is improved from that of the previous version, COGA. COGA-II is subsequently benchmarked against a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto genetic algorithm (SPEA-II), in seven scalable DTLZ benchmark problems. The results reveal that for the closeness to the true Pareto front COGA-II is much better than NSGA-II, and SPEA-II. For diversity of solutions, the diversity of the solutions by COGA-II is comparable to that of SPEA-II, while NSGA-II has poor diversity. COGA-II can also prevent solutions diverging from true Pareto solutions that occur on NSGA-II and SPEA-II for problems with more than 4 objectives. Thus, it can be concluded that COGA-II is suitable for solving an optimization problem with a large number of objectives.
References
- Deb, K., 2001. “Multi-objective Optimization Using Evolutionary Algorithms,” Chichester, UK: Wiley, 2001.
- Deb, K., and Jain, S., 2002. “Running performance metrics for evolutionary multi-objective optimization,” in Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), Singapore, pp. 13-20.
- Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197.
- Deb, K., Thiele L., Laumanns, M., and Zitzler, E., 2005. “Scalable test problems for evolutionary multiobjective optimization” in Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, A. Abraham, L. C. Jain, and R. Goldberg, Eds. London, UK: Springer, 2005, pp. 105- 145.
- Fonseca, C. M.; and Fleming, P. J. 1993. “Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization,” Proceedings of the Fifth International Conference on Genetic Algorithms, 416-423. Urbana-Champaign, IL, USA.
- Igel, C., Suttorp, T., and Hansen, N., 2007. “Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES,” Lecture Notes in Computer Science, vol. 4403, pp. 171-185.
- Keerativuttitumrong, N.; Chaiyaratana, N.; and Varavithya V. 2002. “Multi-objective co-operative coevolutionary genetic algorithm,” Lecture Notes in Computer Science. 2439: 288-297.
- Li, H., and Zhang, Q., 2006. “A multiobjective differential evolution based on decomposition for multiobjective optimization with variable linkages,” Lecture Notes in Computer Science, vol. 4193, pp. 583-592.
- Maneeratana, K., Boonlong, K., and Chaiyaratana, N., 2006. “Compressed-objective genetic algorithm,” Lecture Notes in Computer Science, vol. 4193, pp. 473-482.
- Pierro, F. D., Khu, S. T., and Savic, D. A.m 2007. “An investigation on preference order ranking scheme for multiojective evolutionary optimization,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 17-45.
- Purshouse, R. C. and P. J. Fleming, 2007. “On the evolutionary optimization of many conflicting objectives,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 770-784.
- Soylu, B., and Köksala, M., “A favorable weight-based evolutionary algorithm for multiple criteria problems” 2010. IEEE Transactions on Evolutionary Computation, vol. 14, no. 2, pp. 191-205.
- Srinivas, N.; and Deb, K. 1994. “Multi-objective function optimization using non-dominated sorting genetic algorithms,” Evolutionary Computation, 2(3): 221- 248.
- Zhang, Q., and Li., H. 2007. “MOEA/D: A multiobjective evolutionary algorithm based on decomposition” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731.
- Zhou, A., Zhang, Q., Jin, Y,. and Sendhoff, B., 2007. “Adaptive modelling strategy for continuous multiobjective optimization,” in Proceedings of the 2007 Congress on Evolutionary Computation (CEC'07), pp. 431-437
- Zitzler, E., Deb, K., and Thiele, L., 2000. “Comparison of multiobjective evolutionary algorithms: Empirical results,” Evolutionary Computation, vol. 8, no. 2, pp. 173-195.
- Zitzler, E., Laumanns, M., and Thiele, L., 2002. “SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization,” in Evolutionary Methods for Design, Optimisation and Control, K. Giannakoglou, D. Tsahalis, J. Periaux, K. Papailiou, and T. Fogarty, Eds. Barcelona, Spain: CIMNE, pp. 95-100.
- Zitzler, E., and Thiele, L. 1999. “Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation. 3(4): 257- 271.
Paper Citation
in Harvard Style
Boonlong K., Chaiyaratana N. and Maneeratana K. (2010). IMPROVED COMPRESSED OBJECTIVE GENETIC ALGORITHM: COGA-II . In Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) ISBN 978-989-8425-31-7, pages 95-103. DOI: 10.5220/0003086700950103
in Bibtex Style
@conference{icec10,
author={Kittipong Boonlong and Nachol Chaiyaratana and Kuntinee Maneeratana},
title={IMPROVED COMPRESSED OBJECTIVE GENETIC ALGORITHM: COGA-II},
booktitle={Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)},
year={2010},
pages={95-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003086700950103},
isbn={978-989-8425-31-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)
TI - IMPROVED COMPRESSED OBJECTIVE GENETIC ALGORITHM: COGA-II
SN - 978-989-8425-31-7
AU - Boonlong K.
AU - Chaiyaratana N.
AU - Maneeratana K.
PY - 2010
SP - 95
EP - 103
DO - 10.5220/0003086700950103