Classical and Nonclassical Optimization Methods
Published Online: 15 SEP 2006
Copyright © 2000 John Wiley & Sons, Ltd. All rights reserved.
Encyclopedia of Analytical Chemistry
How to Cite
Wehrens, R. and Buydens, L. M. 2006. Classical and Nonclassical Optimization Methods. Encyclopedia of Analytical Chemistry. .
- Published Online: 15 SEP 2006
Optimization problems are abundant in analytical chemistry, examples being the determination of optimal conditions for experiments or optimal settings for instruments. In general, all the required information should be obtained from as few experiments as possible. Classical techniques such as response surface models or simplex optimization are often used. These techniques, which can be very efficient in cases where the underlying assumptions are fulfilled, are called “strong” methods.
With the advent of the computer in the laboratory, a new class of optimization problems arose which could not be tackled with the standard methodologies. For these search-type problems, new strategies such as simulated annealing (SA) and genetic algorithms (GA) are applied. Although these are not guaranteed to give the optimal result, in almost all cases they are able to find very good solutions where other techniques fail completely. These methods find themselves in an intermediate position between the strong methods, mentioned above, and weak methods, where hardly any assumptions are made.
This article provides an overview of classical and nonclassical optimization techniques, and stresses the differences in their areas of application. The key ideas are highlighted and references to important publications are given.