Efficient Developments in Modeling and Optimization of Solid State Fermentation

Bouaoudat, Badia Dandach; Yalaoui, Farouk; Entzmann, Françoise

doi:10.5504/bbeq.2012.0108

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…[15] PSO shares many similarities with evolutionary computation techniques such as GA. [16] Both methods are issued from artificial intelligence which makes them possible to handle highly nonlinear problems based on efficient stochastic optimization formalisms. [17] Both are population-based search approaches and they depend on information shared among their population members to improve their search processes using probabilistic rules. [18] However, unlike GA, PSO has no evolution operators such as crossover and mutation.…”

Section: Introductionmentioning

confidence: 99%

Comparison across growth kinetic models of alkaline protease production in batch and fed-batch fermentation using hybrid genetic algorithm and particle swarm optimization

Ghovvati

Khayati

Attar

et al. 2015

Biotechnology & Biotechnological Equipment

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The aim of this study was to estimate the kinetic parameters of alkaline protease production with consideration of different growth kinetic models in order to establish the most adequate one to describe the bioprocess dynamics in both batch and fed-batch modes. As a result, a particular method for parameter estimation is developed in this paper. In this method, a hybrid of two metaheuristic techniques, genetic algorithm (GA) and particle swarm optimization (PSO), which takes advantage of both techniques, is applied. In the suggested hybrid algorithm, GA provides the initial population for PSO and then PSO performs the improvement task. The method needs low-intensive computation and proved to be superior to the traditional methods. As a result of applying this method, it was found that the Contois model, in spite of its simplicity, provides a satisfactory agreement with the experimental data, whereas the adoption of more detailed models leads to negligible improvements of the fit. Finally, for comparison of performance of the hybrid algorithm, we used GA only and PSO only. It was shown that the proposed hybrid between meta-heuristics GA and PSO is more effective in terms of running time (two-fold faster) and solution quality, since it benefits from synergy. Also it was concluded that PSO performed slightly better than GA.

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Section: Introductionmentioning

confidence: 99%