Abstract
Recently, multi-objective optimization by use of the genetic algorithms (GAs) is getting a growing interest as a novel approach to this problem. Population based search of GA is expected to find the Pareto optimal solutions of the multi-objective optimization problems in parallel. To achieve this goal, it is an intrinsic requirement that the evolution process of GA maintains well the diversity of the population in the Pareto optimality set. In this paper, the authors propose to utilize the Thermodynamical Genetic Algorithm (TDGA), a genetic algorithm that uses the concepts of the entropy and the temperature in the selection operation, for multi-objective optimization. Combined with the Pareto-based ranking technique, the computer simulation shows that TDGA can find a variety of Pareto optimal solutions.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
J. H. Holland: Adaptation in Natural and Artificial Systems, The University of Michigan (1975).
D. E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley (1989).
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi: Optimization by simulated annealing, Science, Vol. 220, pp. 671–680 (1983).
N. Mori, J. Yoshida, H. Tamaki, H. Kita and Y. Nishikawa: “A Thermodynamical Selection Rule for the Genetic Algorithm,” Proc. of 2nd IEEE Conference on Evolutionary Computation (ICEC'95), pp. 188–192 (1995).
K. Maekawa, N. Mori, H. Tamaki, H. Kita and Y. Nishikawa: “A Genetic Solution for the Traveling Salesman Problem by Means of a Thermodynamical Selection Rule,” Proc. of 3rd IEEE Conference on Evolutionary Computation (ICEC'96), pp. 529–534 (1996).
N. Mori, H. Kita and Y. Nishikawa: “Adaptation to a Changing Environment by Means of the Thermodynamical Genetic Algorithm” (accepted by PPSN'96).
T. Fukao: Thermodynamical Theory of Distributed System, Shoukoudou (1987, in Japanese).
J. D. Schaffer: “Multiple objective optimization with vector evaluated genetic Algorithms,” Proc. of 1st Int. Conf. on Genetic Algorithms and Their Applications, pp. 93–100 (1985).
C. M. Fonseca and P. J. Fleming: “Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization,” Proc. of 5th Int. Conf. on Genetic Algorithms, pp. 416–423 (1993).
J. Horn, N. Nafpliotis and D. E. Goldberg: “A niched Pareto genetic algorithm for multiobjective optimization,” Proc. of 1st IEEE Conf. on Evolutionary Computation, pp. 82–87, (1994).
N. Srinivas and K. Deb: “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary Computation, Vol. 2, No. 3, pp. 221–248 (1994).
C. M. Fonseca and P. J. Fleming: “An Overview of Evolutionary Algorithms in Multiobjective Optimization,” Evolutionary Computation, Vol. 3, No. 1, pp. 1–16 (1995).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kita, H., Yabumoto, Y., Mori, N., Nishikawa, Y. (1996). Multi-objective optimization by means of the thermodynamical genetic algorithm. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1014
Download citation
DOI: https://doi.org/10.1007/3-540-61723-X_1014
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61723-5
Online ISBN: 978-3-540-70668-7
eBook Packages: Springer Book Archive