Summary
In optimization studies, often researchers are interested in finding one or more optimal or near-optimal solutions. In this chapter, we describe a systematic optimization-cum-analysis procedure which performs a task beyond simply finding optimal solutions, but first finds a set of near-Pareto-optimal solutions and then analyses them to unveil salient knowledge about properties which make a solution optimal. The proposed ‘innovization’ task is explained and its working procedure is illustrated on a number of engineering design tasks. The variety of problems chosen in the chapter and the resulting innovations obtained for each problem amply demonstrate the usefulness of the proposed innovization task. The procedure is a by-product of performing a routine multiobjective optimization for a design task and in our opinion portrays an important process of knowledge discovery which may not be possible to achieve by other means.
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
H.P. Benson. Existence of efficient solutions for vector maximization problems. Journal of Optimization Theory and Applications, 26(4):569–580, 1978.
V. Chankong and Y. Y. Haimes. Multiobjective Decision Making Theory and Methodology. New York: North-Holland, 1983.
C. A. C. Coello, D. A. VanVeldhuizen, and G. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Boston, MA: Kluwer Academic Publishers, 2002.
K. Deb. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4):311–338, 2000.
K. Deb. Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley, 2001.
K. Deb. Unveiling innovative design principles by means of multiple conflicting objectives. Engineering Optimization, 35(5):445–470, 2003.
K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.
K. Deb and H. Gupta. Searching for robust Pareto-optimal solutions in multi-objective optimization. In Proceedings of the Third Evolutionary Multi-Criteria Optimization (EMO-05) Conference (Also Lecture Notes on Computer Science 3410), pages 150–164, 2005.
K. Deb and N. Gupta. Optimal operating conditions for overhead crane maneuvering using multi-objective evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO-2004), pages 1042–1053, 2004. Lecture Notes in Computer Science (LNCS) 3102.
K. Deb and A. Kumar. Real-coded genetic algorithms with simulated binary crossover: Studies on multi-modal and multi-objective problems. Complex Systems, 9(6):431–454, 1995.
D. E. Goldberg. The design of innovation: Lessons from and for Competent genetic algorithms. Kluwer Academic Publishers, 2002.
J. Jahn. Vector optimization. Berlin, Germany: Springer-Verlag, 2004.
P. Korhonen, S. Salo, and R. Steuer. A heuristic for estimating nadir criterion values in multiple objective linear programming. Operations Research, 45(5):751–757, 1997.
S. Ltd. TEFC 3 phase squirrel cage induction motor catalogue, http://globaludyog.com/pumps/sl.htm.
A. Messac and C. A. Mattson. Normal constraint method with guarantee of even representation of complete pareto frontier. AIAA Journal, in press.
K. Miettinen. Nonlinear Multiobjective Optimization. Kluwer, Boston, 1999.
G. V. Reklaitis, A. Ravindran, and K. M. Ragsdell. Engineering Optimization Methods and Applications. New York : Wiley, 1983.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Deb, K., Srinivasan, A. (2008). Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-72964-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72963-1
Online ISBN: 978-3-540-72964-8
eBook Packages: Computer ScienceComputer Science (R0)