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African Journal of Biotechnology Vol. 10(72), pp. 16120-16127, 16 November, 2011 Available online at http://www.academicjournals.org/AJB DOI: 10.5897/AJB11.410 ISSN 1684–5315 © 2011 Academic Journals Review Food processing optimization using evolutionary algorithms Abimbola M Enitan 1 * and Josiah Adeyemo 2 1 Department of Biotechnology and Food Technology, Durban University of Technology, P.O Box 1334, Durban, 4000, South Africa. 2 Department of Civil Engineering and Surveying, Durban University of Technology, P.O Box 1334, Durban, 4000, South Africa. Accepted 12 October, 2011 Evolutionary algorithms are widely used in single and multi-objective optimization. They are easy to use and provide solution(s) in one simulation run. They are used in food processing industries for decision making. Food processing presents constrained and unconstrained optimization problems. This paper reviews the development of evolutionary algorithm techniques as used in the food processing industries. Some evolutionary algorithms like genetic algorithm, differential evolution, artificial neural networks and fuzzy logic were studied with reference to their applications in food processing. Several processes involved in food processing which include thermal processing, food quality, process design, drying, fermentation and hydrogenation processes are discussed with reference to evolutionary optimization techniques. We compared the performances of different types of evolutionary algorithm techniques and suggested further areas of application of the techniques in food processing optimization. Key words: Evolutionary algorithms, optimization, food processing, multi-objective, constrained and unconstrained. INTRODUCTION Evolutionary algorithms (EAs) are computational-based biological-inspired optimization algorithms. They are stochastic searching methods, commonly used for solving non-differentiable, non-continuous and multimodal optimization problems based on Darwin’s natural selection principle. They imitate the process of natural evolution and are becoming important optimization tools for finding the global optimum solutions in several real world applications. EAs operate on a population of potential solutions, applying the principle of survival of the fittest to produce successful and better solution by means of evolutionary resembling operations (selection, reproduction and mutation), which are applied on individuals in a population (Ronen et al., 2002). EAs are widely used for single and multi-objective optimization in food processing. Modern day food processing involves a lot of *Corresponding author. E-mail: enitanabimbola@gmail.com. Tel: +27313732895. Fax: +27313732816. decision making resulting in many objective functions and constraints. EAs can generate Pareto optimal solutions for these models. Most manufacturing industries are in a continuous effort to increase their profits and reduce their production costs due to the strong competition that exists among them. Food processing as an aspect of biotechnology is recently facing remarkable challenges revolving around maximizing profit in a dynamic and an uncertain environment, while satisfying a variety of constraints such as quality of final product, financial, environmental, safety and human constraints. In response to such challenges, food industries are trying to improve process operations by using better technology. Processing optimization includes a performance evaluation function, control variables, constraints and a mathematical model (Evans, 1982). Capitalizing on newly available technologies, the food industries have recently started using sophisticated technologies to improve, monitor, optimize and control food processing parameters such as moisture content,
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