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Improving food processing using modern optimization methods

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TLDR
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.
Abstract
In this contribution, computer-aided optimization is presented as the ultimate tool to improve food processing. The state of the art is reviewed, especially focusing in recent developments using modern optimization techniques. Their potential for industrial applications is also discussed in the light of several important examples. Finally, future trends and research needs are outlined.

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Journal ArticleDOI

A review of recent advances in global optimization

TL;DR: This paper presents an overview of the research progress in deterministic global optimization during the last decade (1998-2008).
Journal ArticleDOI

Thermal processing and quality: Principles and overview

TL;DR: The food processing industry has matured over the years with an impressive record on safety and a vibrant marketplace for new product development as discussed by the authors, however, the need to optimize quality and operating costs could potentially prompt re-evaluating existing systems to capture additional benefits.
Journal ArticleDOI

Advanced computational modelling for drying processes – A review

TL;DR: In this paper, the authors deal with rapidly emerging advanced computational methods for modeling dehydration of porous materials, particularly for foods, and they show promising perspectives to aid developing next-generation sustainable and green drying technology, tailored to the new requirements for the future society, and are expected to play an increasingly important role in drying technology research.
Journal ArticleDOI

Global optimization in the 21st century: Advances and challenges

TL;DR: This paper presents an overview of the research progress in global optimization during the last 5 years, and a brief account of the recent research contributions, and the recently proposed novel generalized BB framework.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.

Genetic algorithms in search, optimization and machine learning

TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Book

Genetic Algorithms

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