Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Inhaltsverzeichnis
Reference
Parmee, I.C., Cvetković, D.C., Watson, A.H., Bonham, C.R.: Multiobjective Satisfaction within an Interactive Evolutionary Design Environment. Evol. Comput. 8(2), 197–222 (2000),
DOI
http://dx.doi.org/10.1162/106365600568176
Abstract
The paper introduces the concept of an Interactive Evolutionary Design System (IEDS) that supports the engineering designer during the conceptual/preliminary stages of the design process. Requirement during these early stages relates primarily to design search and exploration across a poorly defined space as the designer's knowledge base concerning the problem area develops. Multiobjective satisfaction plays a major role, and objectives are likely to be ill-defined and their relative importance uncertain. Interactive evolutionary search and exploration provides information to the design team that contributes directly to their overall understanding of the problem domain in terms of relevant objectives, constraints, and variable ranges. This paper describes the development of certain elements within an interactive evolutionary conceptual design environment that allows off-line processing of such information leading to a redefinition of the design space. Such redefinition may refer to the inclusion or removal of objectives, changes concerning their relative importance, or the reduction of variable ranges as a better understanding of objective sensitivity is established. The emphasis, therefore, moves from a multiobjective optimization over a preset number of generations to a relatively continuous interactive evolutionary search that results in the optimal definition of both the variable and objective space relating to the design problem at hand. The paper describes those elements of the IEDS relating to such multiobjective information gathering and subsequent design space redefinition.
Extended Abstract
Bibtex
Used References
Banzhaf, W., Daida, J., and et al., editors (1999). GECCO–99: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA. Morgan Kaufmann.
Ben-Tal, A. (1979). Characterisation of Pareto and lexicographic optimal solutions. In Fandel, G. and Gal, T., editors, Proceedings of the Third Conference on Multiple Criteria Decision Making Theory and Application, pages 1–11. Springer Verlag.
Chen, S.-J., Hwang, C.-L., and Hwang, F. P. (1992). Fuzzy Multiple Attribute Decision Making. Springer-Verlag.
Cvetkovi ́c, D. and Parmee, I. C. (1999). Use of preferences for ga–based multi–objective optimi- sation. In (Banzhaf et al., 1999), pages 1504–1510.
Cvetkovi ́c, D. and Parmee, I. C. (2000). Designer’s preferences and multi–objective preliminary design process. In Parmee, I. C., editor, Proceedings of the Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM’2000). PEDC, University of Plymouth, UK.
Deb, K. (1999). Evolutionary algorithms for multi–criterion optimization in engineering de- sign. In Proceedings of Evolutionary Algorithms in Engineering and Computing Science (EUROGEN-99).
Fodor, J. and Roubens, M. (1994). Fuzzy Preference Modelling and Multicriteria Decision Sup- port. Kluwer Academic Publishers.
Fonseca, C. M. and Fleming, P. J. (1995). An overview of evolutionary algorithms in multiobjec- tive optimization. Evolutionary Computation, 3(1):1–16.
Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, R., and Sunderam, V. (1994). PVM: Parallel Virtual Machine A Users’ Guide and Tutorial for Networked Parallel Computing. MIT Press, Cambridge, Massachusetts.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison–Wesley.
Hajela, P. and Lin, C. Y. (1992). Genetic search strategies in multicriterion optimal design. Struc- tural Optimisation, 4:99–107.
Horn, J. and Nafpliotis, N. (1993). Multiobjective optimization using the niched Pareto genetic algorithm. IlliGAL Report 93005, Illinois Genetic Algorithm Laboratory.
Hwang, C.-L. and Masud, A. S. M. (1979). Multiple Objective Decision Making – Methods and Applications. Springer Verlag, Berlin.
Jakob, W., Gorges-Schleuter, M., and Blume, C. (1992). Application of genetic algorithms to task planning and learning. In PPSN II, pages 291–300. North–Holland.
Kursawe, F. (1991). A variant of evolution strategies for vector optimization. In Schwefel, H.-P. and M ̈anner, R., editors, Parallel Problem Solving from Nature — Proc. 1st Workshop PPSN, volume 496 of Lecture Notes in Computer Science, pages 193–197, Berlin. Springer.
Lai, Y.-J. and Hwang, C.-L. (1996). Fuzzy Multiple Objective Decision Making. Springer-Verlag. Lin, J. G. (1976). Maximal vectors and multi–objective optimization. Journal of Optimization Theory and Application, 18(1):41–64.
Nisbett, R. E. and Wilson, T. D. (1977). Telling more then we can know: Verbal reports on mental processes. Psychological Review, 84(3):231–259.
Osyczka, A. (1984). Multicriterion Optimization in Engineering with FORTRAN Programs. Ellis Horwood.
Parmee, I. C. (1996). The maintenance of search diversity for effective design space decomposition using cluster oriented genetic algorithms (COGAs) and multi–agent strategies (GAANT). In
Parmee, I. C., editor, Proceedings of Adaptive Computing in Engineering Design and Control, pages 128–138. PEDC, University of Plymouth, UK.
Parmee, I. C. (1999). Exploring the design potential of evolutionary search, exploration and opti- misation. In Bentley, P. J., editor, Evolutionary Design by Computers. Morgan Kaufmann.
Parmee, I. C. and Bonham, C. R. (1998). Supporting innovative and creative design using interac- tive designer / evolutionary computing strategies. In Computation Models of Creative Design Conference, Heron Island, Australia. University of Sydney.
Parmee, I. C. and Watson, A. H. (1999). Preliminary airframe design using co–evolutionary mul- tiobjective genetic algorithms. In (Banzhaf et al., 1999), pages 1657–1665.
Peace, G. S. (1993). Taguchi Methods: A Hands–On Approach. Addison–Wesley.
Schaffer, J. D. (1984). Some Experiments in Machine Learning using Vector Evaluated Genetic Algorithm. PhD thesis, Vanderbilt University, Nashville. TCGA file No. 00314.
Srinivas, N. and Deb, K. (1995). Multiobjective optimization using nondominated sorting in ge- netic algorithms. Evolutionary Computation, 2(3):221–248.
Veldhuizen, D. A. V. (1999). Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Air Force Institute of Technology, Wright–Paterson AFB.
Warshall, S. (1962). A theorem on Boolean matrices. Journal of the ACM, 9(1):11–12.
Links
Full Text
http://cvele.org/dcpapers/pedc_ec2000.ps.gz