Global Optimization
Description
Optimization models based on a nonlinear systems description often possess multiple local optima. The objective of global optimization (GO) is to find the best possible solution of multiextremal problems. This volume illustrates the applicability of GO modeling techniques and solution strategies to real-world problems.
The contributed chapters cover a broad range of applications from agroecosystem management, assembly line design, bioinformatics, biophysics, black box systems optimization, cellular mobile network design, chemical process optimization, chemical product design, composite structure design, computational modeling of atomic and molecular structures, controller design for induction motors, electrical engineering design, feeding strategies in animal husbandry, the inverse position problem in kinematics, laser design, learning in neural nets, mechanical engineering design, numerical solution of equations, radiotherapy planning, robot design, and satellite data analysis. The solution strategies discussed encompass a range of practically viable methods, including both theoretically rigorous and heuristic approaches.
Reviews / Votes
From the reviews:
"The general subject of global optimization deals with finding the overall minimum of a nonlinear, non-convex objective function, subject to nonlinear, non-convex equality and inequality constraints . . This collection should thus be of use to researchers in algorithms and theory of global optimization, especially those involved in consulting or on teams dealing with practical models. . The collection should also be of use to researchers in particular applications and other consumers of optimization results . ." (Ralph Baker Kearfott, Journal of Global Optimization, Vol. 38, 2007)
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