Abstract
This paper deals with multi-attribute classification problem based on dynamical multi-objective optimization approaches. The matching of attribute is seen as objective of the problem and user preferences are uncertain and changeable. Traditional sum weighted method and simple evolutionary algorithm are employed for experimental study over practical industry product classification problems. A integrate system framework is proposed to realize the dynamical model for multi-objective optimization. The experimental results show that classification performance system can be improved under the dynamical system framework according to user preference.
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.
References
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. John Wiley & Sons, Chichester (2001)
Marler, R.T., Arora, J.S.: Survey of Multi-Objective Optimization Methods for Engineering. Struct. Multidisc Optim. 26, 369–395 (2004)
Amanifard, N., Nariman-Zadeh, N., Borji, M., Khalkhali, A., Habibdoust, A.: Modelling and Pareto Optimization of Heat Transfer and Flow Coefficients in Microchannels using GMDH type neural networks and genetic algorithms. Energy Conversion and Management 49(2), 311–325 (2008)
Amodeo, L., Chen, H., Hadji, A.E.: Multi-objective Supply Chain Optimization: An Industrial Case Study. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 732–741. Springer, Heidelberg (2007)
Aranha, C., Iba, H.: Modelling Cost into a Genetic Algorithm-Based Portfolio Optimization System by Seeding an Objective Sharing. In: 2007 IEEE Congress on Evolutionary Computation, pp. 196–203. IEEE Press, Singapore (2007)
Askar, S.S., Tiwari, A.: Finding Exact Solutions for Multi-Objective Optimisation Problems using a Symbolic Algorithm. In: 2009 IEEE Congress on Evolutionary Computation, pp. 24–30. IEEE Press, Trondheim (2009)
Avigad, G., Moshaiov, A., Brauner, N.: MOEA-Based Approach to Delayed Decisions for Robust Conceptual Design. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 584–589. Springer, Heidelberg (2005)
Bader, J., Brockhoff, D., Welten, S., Zitzler, E.: On Using Populations of Sets in Multiobjective Optimization. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 140–154. Springer, Heidelberg (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
Wang, L., Li, Y. (2010). Dynamical Multi-objective Optimization Using Evolutionary Algorithm for Engineering. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-16493-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
eBook Packages: Computer ScienceComputer Science (R0)