Journal ArticleDOI
Application of stochastic global optimization algorithms to practical problems
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Eight recently developed stochastic global optimization algorithms representing controlled random search, simulated annealing, and clustering are used to solve global optimization problems from three different fields representing many-body potentials in physical chemistry, optimal control of a chemical reactor, and fitting a statistical model to empirical data.Abstract:
We describe global optimization problems from three different fields representing many-body potentials in physical chemistry, optimal control of a chemical reactor, and fitting a statistical model to empirical data. Historical background for each of the problems as well as the practical significance of the first two are given. The problems are solved by using eight recently developed stochastic global optimization algorithms representing controlled random search (4 algorithms), simulated annealing (2 algorithms), and clustering (2 algorithms). The results are discussed, and the importance of global optimization in each respective field is focused.read more
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Journal ArticleDOI
Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods
TL;DR: Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.
Journal ArticleDOI
A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems
TL;DR: A collection of test problems, some are better known than others, provides an easily accessible collection of standard test problems for continuous global optimization and investigates the microscopic behavior of the algorithms through quartile sequential plots.
Journal ArticleDOI
Population set-based global optimization algorithms: some modifications and numerical studies
M. Montaz Ali,Aimo Törn +1 more
TL;DR: This paper studies the efficiency and robustness of some recent and well known population set-based direct search global optimization methods such as Controlled Random Search, Differential Evolution and the Genetic Algorithm.
Global optimization and simulated annealing
TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
Journal ArticleDOI
Improving food processing using modern optimization methods
TL;DR: In this contribution, computer-aided optimization is presented as the ultimate tool to improve food processing, especially focusing in recent developments using modern optimization techniques.
References
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TL;DR: In this article, the authors proposed a truncated icosahedron, a polygon with 60 vertices and 32 faces, 12 of which are pentagonal and 20 hexagonal.
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Book
Global Optimization: Deterministic Approaches
Reiner Horst,Tuy Hoang +1 more
TL;DR: This study develops a unifying approach to constrained global optimization that provides insight into the underlying concepts and properties of diverse techniques recently proposed to solve a wide variety of problems encountered in the decision sciences, engineering, operations research and other disciplines.