Abstract
A parallel distributed optimization method for the minimization of the total resource of a system with discrete elements is proposed, and theoretical and experimental verifications are carried out in this paper. The distributed optimization algorithm consists of two processes, namely the resource reduction process and the resource addition process. In the former process, each element discards its critical resource margin which is the minimum among the resource margins with respect to global and local constrainsts while in the latter process, a small amount of resources are added to all the elements. Some rules for adjusting the additional resources are introduced to obtain fast convergence and better solutions. The proposed method is sucessively applied for optimizing electric circuits and discrete structures, and the method is found to be effective, very robust and suitable for parallel processing. The proposed distributed optimization algorithm is found heuristically, but its effectiveness is also analyzed theoretically.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Woodson, M., Johnson, E., and Haftka, R.: Optimal Design of Composite Fuselage Frames for Progressive Failure and Energy Absorption by Genetic Agorithms, AIAA Paper 95-1218 (1995)
Leung, M. and Nevill, G. Jr.: Genetic Algorithms for Preliminary 2-D Structural Design, AIAA Paper 94-1602 (1994)
Gage, P.J., Kroo, I.M., and Sobieski, I.P.: Variable-Complexity Genetic Algorithm for Topological Design, AIAA Journal, 33 (1995) 2212–2217
Atiquallah, M.M. and Rao, S.S.: Parallel Processing in Optimal Structural Design Using Simulated Annealing, AIAA Journal, 33 (1995) 2386–2392
McMurtry Adaptive Optimization Procedures, A Prelude to Neural Networks: Adaptive and Learning systems, edited by J.M. Mendel, PTR Prentice Hall, New Jersy (1994) 243–286
Yang, J.B. and Sen P.: An Artificial Neutral Network Approach for Nonlinear Optimization with Discrete Design Variables, Proceedings of 16th IFIP-TC7 Conf., System Modeling and Optimization, Springer-Verlag, Berlin (1993) 761–770.
Najim, K. and Pznyak, A.S.: Learning Automata, Elsevier Science, New York (1994)
Miki, M.: Object-Oriented Optimization of Discrete Structures, AIAA Journal 33, 10 (1995) 1940–1945
Schwefel, H.P.: Evolution and Optimum Seeking, John Wiley & Sons, New York (1995)
Lesser, V.R. and Corkill, D.D.: Distributed Problem Solving, Encyclopedia of Artificial Intelligence edited by S. C. Shapiro, John Wiley & Sons, New York (1987) 245–251
Gordon, V.S., Whitley, D., and Bohm, A.T.W.: Dataflow Parallelism in Genetic Algorithms, Parallel Problem solving from Nature, 2, edited by R. Manner and B. Manderick, Elsevier Science, Amsterdam (1992) 533–542.
Whitley D. and Starkweather, T., GENITOR II: a Distributed Genetic Algorithm, J. Experimental & Theoretical Artificial Intelligence, 2 (1990) 189–214
Mehr, I. and Obrodovic, Z.: Parallel Neural Network Learning Through Repetitive Bounded Depth Trajectory Branching, IEEE Proceedings of the 8th International Parallel Processing Symposium, The IEEE Computer Science Press, Los Alamitos (1994) 784–791
Rumelhart, D.E., Hinton, G.E., and McClelland, J.L.: A General Framework for Parallel Distrubted Processing, Artificial Neural Networks: Concepts and Theory, edited by P. Mehra and B.W. Wah, IEEE Computer Society Press, Los Alamitos (1992) 56–82
Schnabel R.B.: A View of the Limitations, Opportunities, and Challenges in Parallel Nonlinear Optimization, Parallel Computing, 21 (1995) 875–905
Laarhoven, P.J.: Parallel Variable Metric Methods for Unconstrained Optimization, Math. Programming, 33 (1985) 68–81
Dennis Jr., J.E. and Torczon, V.: Direct Search Method Methods on Parallel Computers, SIAM J. Optimization, 1 (1991) 448–474
Nash, A.G. and Sofer, A.: Block Truncated-Newton Methods for Parallel Optimization, Math. Programming, 45 (1989) 529–546
Miki, M.: Object-Oriented Approach to Modeling and Analysis of Truss Structures, AIAA Journal, 33, 2 (1994), 348–354
Miki, M.: Parallel Computing for Analysis of Variable Geometry Truss, AIAA Paper 95-1307 (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miki, M., Hiroyasu, T., Ikeda, T. (1999). Parallel distributed optimization by resource addition and reduction. In: Polychronopoulos, C., Fukuda, K.J.A., Tomita, S. (eds) High Performance Computing. ISHPC 1999. Lecture Notes in Computer Science, vol 1615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0094922
Download citation
DOI: https://doi.org/10.1007/BFb0094922
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65969-3
Online ISBN: 978-3-540-48821-7
eBook Packages: Springer Book Archive