Abstract
A global optimization method is proposed to improve the conventional method of simulated annealing. By introducing the probability distribution function for the objective function and the concept of stable energy for detecting thermal equilibrium during annealing, the selection of initial temperature and equilibrium criterion becomes easy and effective. Furthermore, the efficiency and robustness of the proposed method is retained by employing the technique of region reduction and an adaptive neighborhood structure. In the case where multiple (global) optima may exist, a technique based on the method of simulated evolution is developed to circumvent the difficulty of convergence of population. Numerical studies of some standard test functions and an optimum structural design problem show that the proposed method is effective in solving global optimization problems.
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References
Brooks, K.H.: A Discussion of Random Methods of Seeking Optima. Operations Research 6, 244–251 (1958)
Solis, F.J., Wets, R.J.B.: Minimization by Random Search Techniques. Mathematics of Operations Research 6, 19–30 (1981)
Fabio, S.: Stochastic Techniques for Global Optimization: A survey of Recent Advances. Journal of Global Optimization 1, 207–228 (1991)
Goldberg, D.E.: Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley Reading, MA (1989)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley Publishing, New York (1966)
Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. J. Wiley & Sons, Chichester (1989)
Jenkins, W.M.: Towards Structural Optimization Via the Genetic Algorithm. Computer & Structures 40, 1321–1327 (1991)
Riche, R.L., Haftka, R.T.: Optimization of Laminate Stacking Sequence for Buckling Load Maximization by Genetic Algorithm. AIAA Journal 31, 951–956 (1993)
Junjiro, O., Yoji, H.: Actuator Placement Optimization by Genetic and Improved Simulated Annealing Algorithms. AIAA Journal 31, 1167–1169 (1992)
Atiqullah, M.M., Rao, S.S.: Parallel Processing in Optimal Structural Design Using Simulated Annealing. AIAA Journal 33, 2386–2392 (1995)
Srichander, R.: Efficient Schedules for Simulated Annealing. Engineering Optimization 24, 161–176 (1995)
Jaynes, E.T.: Information Theory and Statistical Mechanics. Physical Reviews 106, 620–630 (1957)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Chiang, D.Y., Huang, S.T.: Modal Parameter Identification Using Simulated Evolution. AIAA Journal 35, 1204–1208 (1997)
Lucidi, S., Piccioni, M.: Random Tunneling by Means of Acceptance-Rejection Sampling for Global Optimization. Journal of Optimization Theory & Applications 62, 255–277 (1989)
Kirsch, U.: Optimum Structural Design, ch. 6. McGraw-Hill, New York (1981)
Hajela, P., Yoo, J.: Constraint Handling in Genetic Search Using Expression Strategies. AIAA Journal 34, 2414–2420 (1996)
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Chiang, D., Moh, J. (2006). A Global Optimization Method Based on Simulated Annealing and Evolutionary Strategy. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_96
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DOI: https://doi.org/10.1007/11816157_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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