Kalivas J.H. 's Adaption of Simulated Annealing to Chemical Optimization PDF

By Kalivas J.H.

ISBN-10: 0444818952

ISBN-13: 9780444818959

Optimization difficulties take place on a regular basis in chemistry. the issues are varied and range from choosing the right wavelength layout for optimum spectroscopic focus predictions to geometry optimization of atomic clusters and protein folding. a number of optimization strategies were explored to unravel those difficulties. whereas so much optimizers preserve the power to find international optima for easy difficulties, few are powerful opposed to neighborhood optima convergence in regards to demanding or huge scale optimization difficulties. Simulated annealing (SA) has proven an excellent tolerance to neighborhood optima convergence and is usually known as a world optimizer. The optimization set of rules has came across large use in different components resembling engineering, machine technological know-how, verbal exchange, photo reputation, operation study, physics, and biology. lately, SA and adaptations on it have proven substantial good fortune in fixing quite a few chemical optimization difficulties. One thrust of this e-book is to illustrate the software of SA in a variety chemical disciplines.

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A. IEEE Circuits and Devices Magazine, Jan. 1989, pp 1- 26. , 1989. Adaption of Simulated Annealing to Chemical Optimization Problems, Ed. H. V. All rights reserved. 25 Chapter 2 Comparison of algorithms for wavelength selection Uwe HOrchner and John H. Kalivas1 Department of Chemistry, Idaho State University, Pocatello, Idaho, 83209 USA 1. INTRODUCTION The simulated annealing (SA) algorithm has proven to be suitable for large scale optimization problems. However, optimization results are limited if applications of SA ignore problem specific issues.

59 Iterations No. 5740 467 414 489 524 258 488 811 348 481 533 383 178 143 444 170 296 449 196 214 150 39 2 2 30 45 2 Ff Gg 544 384 262 601 82 616 873 533 172 316 327 594 1500h0 815 450 373 840 88 676 aMethods 1 use fixed initial subset 1/ 2/ 3/ 4/ 5, (A) GSA, (B) GSAMS, and (C) TA. All other entries used random initial subsets: methods 2 = GSA, 3 = MGSA, 4 = GSAMS, and 5 = TA. bBest found response averaged over 50 runs. 'Number of runs that found the known optimal subset. dAverage number of iterations to find the best subset in each run.

28 (3) pp 209-217 5. O. L. J. of Comp. and Graphical Statistics, v. 21, pp 1087-1092 (1953). (1986). v. 1 (4) pp. 367-384 (1993). 6. 7. W. in Proceedings of SPIE v. E. J. Smith eds, pp 265-274 (18-21 Aug. 1987, San Diego, CA). 24 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. W. W in SPIE Proceedings, v. 1354, pp 144-153 (International Lens Design Conference, 11-14 June, 1990, Monterey, CA). W. W. submitted to J. of Global Optimization, Apr. 1992. , and Woodfin, G. in SPIE Proceedings, v. 485 pp 104-112, (Applications of Artificial Intelligence, Arlington, VA.

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