Abstract
This paper details some of the issues investigated and experiments conducted by the authors in the course of their design of a integrated multi-objective solution and analysis system. The role to which the concept of multiple objectives can be incorporated into the genetic algorithm framework is examined. The composition of a multi-objective fitness function is discussed, as well as its implications for the genetic operators such as selection, crossover and mutation. Current work on such issues as representation of the efficient set by genetic algorithms are reviewed. A non-aggregating randomised selection algorithm is given and illustrated by means of an example. The use of genetic algorithms for the solution of difficult goal programming models is investigated. A representative non-linear goal programming model is solved under various genetic algorithm parameter options in order to demonstrate the type of parameter sensitivity that can occur when using genetic algorithms as a solution tool.
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.
Similar content being viewed by others
References
FONESCA, C.M. and FLEMING, P.J. (1993) Genetic algorithms for multiobjective optimization: Formulation, Discussion, and Generalization, Proceedings of the Fifth Annual Conference on Genetic Algorithms, 416423.
GEN, M., IDA, K., LEE, J. and KIM, J. (1997) Fuzzy Nonlinear Goal programming Using Genetic Algorithm, Computers and Industrial Engineering, 33, 39–42.
GARDINER, L.R. and STEUER, R. (1994) Unified interactive multiple objective programming, European Journal of Operational Research, 74, 391–406.
GOLDBERG, D.E.(1989) Genetic Algorithms in Search, Optimization and Machine Learning,Addison-Wesley.
MICHALEWICZ, Z.(1996) Genetic Algorithms + Data Structures Evolution Programs,3rd Edition, Springer-Verlag.
MURATA, T., ISHIBUCHI, H., and TANAKA, H. (1996) Multi-objective genetic algorithm and its application to flowshop scheduling, Computers and Industrial Engineering, 30, 957–968.
ROMERO, C. (1991) Handbook of Critical Issues in Goal Programming, Pergamon Press, Oxford.
SAKAWA, M., KATO, K., SUNADA, H., and SHIBANO, T.(1997) Fuzzy programming for multiobjective 0–1 programming problems through revised genetic algorithms.
SCHAFFER, J.D. (1985) Multiple objective optimization with vector evaluated genetic algorithms, Proceedings of an International Conference on Genetic Algorithms and Their Applications, 93–100.
SRINIVAS, N. and DEB, K. Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2, 22 1248.
ll] STEUER, R.E.(1986) Multiple Criteria Optimization : Theory, Computation, and Application,John Wiley and Sons.
SURRY, P.D. and RADCLIFFE, N.J. (1997) The COMOGA method: constrained optimisation by multiple objective genetic algorithms, Control and Cybernetics, 26, 391–412. Lawrence Erlbaum.
TAMIZ, M. and JONES, D.F.(1995)`Expanding the flexibility of goal programming via preference modelling techniques’, Omega, 23, 41–48.
VICINI, A. and QUAGLIARELLA, D. (1997) Inverse and direct airfoil design using a multiobjective genetic algorithm, AIAA Journal, 35, 14991505.
ZELENY, M.(1982) Multi-Criteria Decision Making,McGraw Hill.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mirrazavi, S.K., Jones, D.F., Tamiz, M. (2002). Towards the Development of an Integrated Multi-Objective Solution and Analysis System. In: Trzaskalik, T., Michnik, J. (eds) Multiple Objective and Goal Programming. Advances in Soft Computing, vol 12. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1812-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1812-3_13
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1409-5
Online ISBN: 978-3-7908-1812-3
eBook Packages: Springer Book Archive