Abstract
The excellent performance of evolutionary multi-objective algorithms based on S metric selection (SMS) has been identified by many researchers. However, huge computational effort of S metric calculation has limited the full application of those algorithms. This paper proposes a novel S metric selection evolutionary algorithm (SMS-M2M) based on the population decomposition strategy MOEA/D-M2M. In SMS-M2M, SMS is conducted in each subpopulation instead of the whole population, which can avoid the S metric calculation of the total population. The purpose of population decomposition is to directly reduce the huge computational effort of calculating S metric and thus to give a simple but effective method to improve the effectiveness of SMS based algorithm. SMS-M2M utilizes the same SMS with a popular SMS based evolutionary algorithm SMS-EMOA. Numerical studies of SMS-M2M and SMS-EMOA have shown that the M2M population decomposition can effectively reduce the computational effort of SMS, meanwhile the theoretic analysis identifies the efficiency and effectiveness of SMS-M2M.
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
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: Methods and applications. Ph.D. dissertation, Comput. Eng. Netw. Lab., Swiss Federal Instit. Technol (ETH), Zurich, Switzerland (1999)
Purshouse, R.: On the evolutionary optimization of many objectives. Ph.D. dissertation, Dept. Automatic Control Syst. Eng., Univ. Sheffield, Sheffield, U.K (2003)
Laumanns, M., Zitzler, E., Thiele, L.: A unified model for multiobjective evolutionary algorithms with elitism. In: Proc. Congr. Evol. Comput., pp. 46–53 (2000)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective Selection Based on Dominated Hypervolume. European Journal of Operational Research, 1653–1669 (2007)
Fleischer, M.: The Measure of Pareto Optima. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003)
Emmerich, M., Beume, N., Naujoks, B.: An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62–76. Springer, Heidelberg (2005)
Fonseca, C.M., Paquete, L., Lopez-Ibanez, M.: An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator. In: IEEE Congress on Evolutionary Computation (CEC 2006), pp. 1157–1163 (2006)
Knowles, J.D., Corne, D.W., Fleischer, M.: Bounded Archiving using the Lebesgue Measure. In: Congress on Evolutionary Computation (CEC 2003)l, vol. 4, pp. 2490–2497. IEEE Press (2003)
While, L., Bradstreet, L., Barone, L., Hingston, P.: Heuristics for Optimising the Calculation of Hypervolume for Multi-objective Optimisation Problems. In: IEEE Congress on Evolutionary Computation (CEC 2005), pp. 2225–2232 (2005)
While, L., Hingston, P., Barone, L., Huband, S.: A Faster Algorithm for Calculating Hypervolume. IEEE Transactions on Evolutionary Computation, 29–38 (2006)
Liu, H.L., Li, X.: The multiobjective evolutionary algorithm based on determined weight and sub-regional search. In: 2009 IEEE Congress on Evolutionary Computation, Norway, May 18-21, pp. 1928–1934 (2009)
Mei, Y., Tang, K., Yao, X.: Decomposition-Based memetic algorithm for multiobjective capacitated arc routing problem. IEEE Trans. Evol. Comput. 15(2), 151–165 (2011)
Liu, H.L., Gu, F.: A improved NSGA-II algorithm based on subregional search. In: Proc. Congr. Evol. Comput., pp. 1906–1911 (2011)
Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
Liu, H.L., Gu, F., Zhang, Q.: Decomposition of a Multiobjective Optimization Problem into a Number of Simple Multiobjective Subproblems. IEEE Trans. Evol. Comput. (in press, 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, L., Liu, HL., Lu, C., Cheung, Ym., Zhang, J. (2015). A Novel Evolutionary Multi-objective Algorithm Based on S Metric Selection and M2M Population Decomposition. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-13356-0_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13355-3
Online ISBN: 978-3-319-13356-0
eBook Packages: EngineeringEngineering (R0)