Abstract
With the popularity of efficient multi-objective evolutionary optimization (EMO) techniques and the need for such problem-solving activities in practice, EMO methodologies and EMO research and application have received a great deal of attention in the recent past. The first decade of research in EMO area has been spent on developing efficient algorithms for finding a well-converged and well-distributed set of Pareto-optimal solutions, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. In this paper, we address this long-standing issue and suggest an interactive EMO procedure by collating most salient research in EMO and putting together a step-by-step EMO and decision-making procedure. The idea is implemented in a GUI-based, user-friendly software which allows a user to supply the problem mathematically or by using user-defined macros and enables the user to evaluate solutions directly or by calling an executable software, such as popularly-used MATLAB software for a local search or ANSYS software for finite element analysis, etc. Starting with standard EMO applications, continuing to finding robust, partial, and user-defined preferred frontiers through standard MCDM procedures, the well-coordinated software allows the user to first have an idea of the complete trade-off frontier, then systematically focus in preferred regions, and finally choose a single solution for implementation.
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
Branke, J., Kauβler, T., Schmeck, H.: Guidance in Evolutionary Multi-objective Optimization. Advances in Engineering Software 32, 499–507 (2001)
Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. North-Holland, New York (1983)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Chaudhuri, S.: Automated discovery of innovative designs of mechanical components using evolutionary multi-objective algorithms. In: Nedjah, N., M. de Macedo, L. (eds.) Evolutionary Machine Design: Methodology and Applications, pp. 143–168. Nova Science Publishers, New York (2005)
Deb, K., Chaudhuri, S.: I-EMO: An interactive evolutionary multi-objective optimization tool. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 690–695. Springer, Heidelberg (2005)
Deb, K., Goel, T.: A hybrid multi-objective evolutionary approach to engineering shape design. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 385–399. Springer, Heidelberg (2001)
Deb, K., Gupta, H.: Searching for robust Pareto-optimal solutions in multi-objective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 150–164. Springer, Heidelberg (2005)
Deb, K., Jain, S.: Running performance metrics for evolutionary multi-objective optimization. In: Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL-02), pp. 13–20 (2002)
Deb, K., Sundar, J., Rao N., U.B., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research 2(3), 273–286 (2006)
Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms–Part II: Application example. IEEE Transactions on Systems, Man, and Cybernetics: Part A: Systems and Humans 28(1), 38–47 (1998)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)
Reklaitis, G.V., Ravindran, A., Ragsdell, K.M.: Engineering Optimization Methods and Applications. Wiley, New York (1983)
Tan, K.C., Lee, T.H., Khoo, D., Khor, E.F.: A multiobjective evolutionay algorithm toolbox for computer-aided multiobjective optimization. IEEE Transactions on Systems,Man, and Cybernetics - Part B: Cybernetics 31(4), 537–556 (2001)
Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making Theory and Applications, pp. 468–486. Springer, Heidelberg (1980)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Deb, K., Chaudhuri, S. (2007). I-MODE: An Interactive Multi-objective Optimization and Decision-Making Using Evolutionary Methods. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-70928-2_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70927-5
Online ISBN: 978-3-540-70928-2
eBook Packages: Computer ScienceComputer Science (R0)