Abstract
This paper introduces a new constraint-handling method called Inverted-Shrinkable PAES (IS-PAES), which focuses the search effort of an evolutionary algorithm on specific areas of the feasible region by shrinking the constrained space of single-objective optimization problems. IS-PAES uses an adaptive grid as the original PAES (Pareto Archived Evolution Strategy). However, the adaptive grid of IS-PAES does not have the serious scalability problems of the original PAES. The proposed constraint-handling approach is validated with several examples taken from the standard literature on evolutionary optimization.
Keywords
- Genetic Algorithm
- Penalty Function
- Multiobjective Optimization
- Feasible Region
- Multiobjective Optimization Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Thomas Bäck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, 1996.
Salvador Botello, José Luis Marroquín, Eugenio Oñate, and Johan Van Horebeek. Solving Structural Optimization problems with Genetic Algorithms and Simulated Annealing. International Journal for Numerical Methods in Engineering, 45(8):1069–1084, July 1999.
Eduardo Camponogara and Sarosh N. Talukdar. A Genetic Algorithm for Constrained and Multiobjective Optimization. In Jarmo T. Alander, editor, 3rd Nordic Workshop on Genetic Algorithms and Their Applications (3NWGA), pages 49–62, Vaasa, Finland, August 1997. University of Vaasa.
Carlos A. Coello Coello. Theoretical and Numerical Constraint Handling Techniques used with Evolutionary Algorithms: A Survey of the State of the Art. Computer Methods in Applied Mechanics and Engineering, 191(11–12):1245–1287, January 2002.
Carlos A. Coello Coello and Efrén Mezura-Montes. Handling Constraints in Genetic Algorithms Using Dominance-Based Tournaments. In I.C. Parmee, editor, Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), volume 5, pages 273–284, University of Exeter, Devon, UK, April 2002. Springer-Verlag.
Carlos A. Coello Coello. Constraint-handling using an evolutionary multiobjective optimization technique. Civil Engineering and Environmental Systems, 17:319–346, 2000.
Carlos A. Coello Coello. Treating Constraints as Objectives for Single-Objective Evolutionary Optimization. Engineering Optimization, 32(3):275–308, 2000.
Carlos A. Coello Coello and Arturo Hernández Aguirre. Design of Combinational Logic Circuits through an Evolutionary Multiobjective Optimization Approach. Artificial Intelligence for Engineering, Design, Analysis and Manufacture, 16(1):39–53, 2002.
Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.
Kalyanmoy Deb and David E. Goldberg. An Investigation of Niche and Species Formation in Genetic Function Optimization. In J. David Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 42–50, San Mateo, California, June 1989. George Mason University, Morgan Kaufmann Publishers.
Carlos M. Fonseca and Peter J. Fleming. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, California, 1993. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers.
David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, Massachusetts, 1989.
Jeffrey Horn, Nicholas Nafpliotis, and David E. Goldberg. A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, volume 1, pages 82–87, Piscataway, New Jersey, June 1994. IEEE Service Center.
Joshua D. Knowles and David W. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.
Slawomir Koziel and Zbigniew Michalewicz. Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization. Evolutionary Computation, 7(1):19–44, 1999.
Efrén Mezura-Montes and Carlos A. Coello Coello. A Numerical Comparison of some Multiobjective-based Techniques to Handle Constraints in Genetic Algorithms. Technical Report EVOCINV-03-2002, Evolutionary Computation Group at CINVESTAV-IPN, México, D.F. 07300, September 2002. available at: http://www.cs.cinvestav.mx/~EVOCINV/.
Zbigniew Michalewicz and Marc Schoenauer. Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation, 4(1):1–32, 1996.
I. C. Parmee and G. Purchase. The development of a directed genetic search technique for heavily constrained design spaces. In I. C. Parmee, editor, Adaptive Computing in Engineering Design and Control-’94, pages 97–102, Plymouth, UK, 1994. University of Plymouth, University of Plymouth.
Tapabrata Ray, Tai Kang, and Seow Kian Chye. An Evolutionary Algorithm for Constrained Optimization. In Darrell Whitley et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pages 771–777, San Francisco, California, 2000. Morgan Kaufmann.
Tapabrata Ray and K.M. Liew. A Swarm Metaphor for Multiobjective Design Optimization. Engineering Optimization, 34(2):141–153, March 2002.
Jon T. Richardson, Mark R. Palmer, Gunar Liepins, and Mike Hilliard. Some Guidelines for Genetic Algorithms with Penalty Functions. In J. David Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA-89), pages 191–197, San Mateo, California, June 1989. George Mason University, Morgan Kaufmann Publishers.
T.P. Runarsson and X. Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284–294, September 2000.
J. David Schaffer. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pages 93–100. Lawrence Erlbaum, 1985.
Patrick D. Surry and Nicholas J. Radcliffe. The COMOGA Method: Constrained Optimisation by Multiobjective Genetic Algorithms. Control and Cybernetics, 26(3):391–412, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hernández Aguirre, A., Botello Rionda, S., Lizárraga Lizárraga, G., Coello Coello, C.A. (2003). IS-PAES: A Constraint-Handling Technique Based on Multiobjective Optimization Concepts. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_6
Download citation
DOI: https://doi.org/10.1007/3-540-36970-8_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-01869-8
Online ISBN: 978-3-540-36970-7
eBook Packages: Springer Book Archive