Variable retort temperature optimization using adaptive random search techniques

Simpson, Ricardo; Abakarov, A.; Teixeira, Arthur A.

doi:10.1016/j.foodcont.2007.10.010

Cited by 23 publications

(14 citation statements)

References 8 publications

(13 reference statements)

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“…The multiobjective optimization approach used in this study is based on optimizing the following aggregating functions by using the adaptive random search method (Sushkov 1969; Abakarov and Sushkov 2002; Simpson and others 2008; Abakarov and others 2009).…”

Section: Methodsmentioning

confidence: 99%

“…This problem has been solved by a series of optimization techniques, but only in the context of the single‐objective optimization problem. These techniques include the gradient‐based methods (Vassiliadis and others 1994), the stochastic method known as integrated controlled random search (ICRS) (Banga and Casares 1987), genetic algorithms (Chen and Ramaswamy 2002) and the adaptive random search method (ARSM) (Simpson and others 2008).…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimization Approach: Thermal Food Processing

Abakarov¹,

Sushkov²,

Almonacid³

et al. 2009

Journal of Food Science

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The objective of this study was to utilize a multiobjective optimization technique for the thermal sterilization of packaged foods. The multiobjective optimization approach used in this study is based on the optimization of well-known aggregating functions by an adaptive random search algorithm. The applicability of the proposed approach was illustrated by solving widely used multiobjective test problems taken from the literature. The numerical results obtained for the multiobjective test problems and for the thermal processing problem show that the proposed approach can be effectively used for solving multiobjective optimization problems arising in the food engineering field.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Approach: Thermal Food Processing

Abakarov¹,

Sushkov²,

Almonacid³

et al. 2009

Journal of Food Science

Self Cite

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show abstract

“…A wide set of experiments and practice implementations of the basic random search algorithm have been conducted, in order to show adaptive random search algorithm's effectiveness and to make recommendations for choosing heuristic parameter values (Abakarov and Sushkov, 2002;Abakarov and Sushkov, 2005;Simpson et al, 2008). However, the following reasons prompted us to propose:…”

Section: Proposed Modification Of Adaptive Random Searchmentioning

confidence: 99%

“…However, in this case the use cubic spline in approaching global optimization problems with random search techniques can produce superior results over discrete step-wise functions (Simpson et al, 2008), mainly, because the cubic spline approximation allows to significantly re- A. Abakarov et al / Journal of Food Engineering 93 (2009) 200-209 duce the number of decision variables, and therefore, the random search.…”

Section: Process Optimization and Computer Simulationmentioning

confidence: 99%

Thermal processing optimization through a modified adaptive random search

Abakarov¹,

Sushkov²,

Almonacid³

et al. 2009

Journal of Food Engineering

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“…Recent works arising from the modelling of thermal processing of foods (like e.g. those of Balsa-Canto et al, 2002a,b;Chalabi et al, 1999;Chen and Ramaswamy, 2002;García et al, 2006;Miri et al, 2008;Simpson et al, 2008) deal fundamentally with optimal control problems where the use of a single performance index (objective function or criteria) is considered for the optimization.…”

Section: Introductionmentioning

confidence: 99%

Efficient and robust multi-objective optimization of food processing: A novel approach with application to thermal sterilization

Sendín

Alonso

Banga

2010

Journal of Food Engineering

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a b s t r a c tThe optimization of thermal processing of foods is a topic which has received great attention during the last decades. The majority of the authors have considered the use of single-objective (criteria) for the optimization. However, the simultaneous optimization of several objectives is much more realistic and desirable, but the associated non-linear programming problems can be very challenging to solve. Here, we describe an efficient and robust multi-criteria optimization method which can be successfully applied to large dynamic systems, like those arising from the modelling of thermal processing of foods. Further, their capabilities for better design and operation of these processes will be highlighted with selected case studies, where the generated Pareto solutions will be analysed. Finally, we will also illustrate their advantages over other recently proposed strategies.

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Variable retort temperature optimization using adaptive random search techniques

Cited by 23 publications

References 8 publications

Multiobjective Optimization Approach: Thermal Food Processing

Multiobjective Optimization Approach: Thermal Food Processing

Thermal processing optimization through a modified adaptive random search

Efficient and robust multi-objective optimization of food processing: A novel approach with application to thermal sterilization

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