Search

The water cycle algorithm (WCA), which is a metaheuristic method inspired by the movements of rivers and streams towards the sea in nature, has been adapted and applied here for the first time for solving such a challenging problem as the parameter identification of fermentation process (FP) models. Bacteria and yeast are chosen as representatives of FP models that are subjected to parameter identification due to their impact in different industrial fields. In addition, WCA is considered in comparison with the genetic algorithm (GA), which is another population-based technique that has been proved to be a promising alternative of conventional optimisation methods. The obtained results have been thoroughly analysed in order to outline the advantages and disadvantages of each algorithm when solving such a complicated real-world task. A discussion and a comparative analysis of both metaheuristic algorithms reveal the impact of WCA on model identification problems and show that the newly applied WCA outperforms GA with regard to the model accuracy.

Parameter identification of non-linear dynamic processes, among them fermentation ones, is rather difficult and non-trivial task to be solved. Failure of conventional optimization methods to provide a satisfactory solution provokes the idea some stochastic algorithms to be tested. As such, the promising metaheuristic algorithm Cuckoo search (CS) has been adapted and applied for a first time to a parameter identification of S. cerevisiae fed-batch fermentation process model. Aiming to improve the model accuracy and the algorithm convergence time, several pre-tests adjustments of CS have been done according to the specific optimization problem. Obtained results confirm the effectiveness and efficacy of the applied CS algorithm. In addition, a comparison between CS and simple genetic algorithm, proved as successful in parameter identification of fermentation process model, has been done. Algorithms advantages and disadvantages have been outlined and the more reliable one have been distinguished.

Contemporary InterCriteria analysis (ICrA) approach for searching of existing or unknown correlations between multiple objects against multiple criteria is applied here. Altogether five different algorithms for InterCriteria relations calculation have been examined to render the influence of the genetic algorithm parameters on the algorithm performance. Two cases, i.e. the model parameter identification of E. coli and S. cerevisiae fed-batch fermentation processes, are considered. In this investigation \(\mu \)-biased, Balanced, \(\nu \)-biased, Unbiased, as well as the newly elaborated and proposed here Weighted algorithm have been consequently applied and thoroughly examined. The obtained results for considered here two Case studies have been compared showing that the most reliable algorithm is the \(\mu \)-biased one.

InterCriteria Analysis is a recently developed approach for the evaluation of the correlation between multiple objects against multiple criteria. As such, it is expected to prove any existing correlations between the criteria themselves or even to discover any new. In this investigation different algorithms for InterCriteria relations calculation are explored to render the influence of the genetic algorithm (GA) parameters on the algorithm performance. GA is chosen as an optimization technique as they are among the most widely used out of the biologically inspired approaches for global search. GA is here applied to parameter identification of a S. cerevisiae fed-batch fermentation process model.

Recently developed approach of InterCriteria Analysis is here applied aiming at an assessment of the performance of such a promising stochastic optimization technique as simple genetic algorithms. Considered algorithms, as representatives of the biologically-inspired ones, are chosen as an object of investigation since they are proven as quite successful in solving of many challenging problems in the field of complex dynamic systems optimization. In this investigation simple genetic algorithms are applied for the purposes of parameter identification of a fermentation process. Altogether six simple genetic algorithms are here considered, differ from each other in the execution order of main genetic operators, namely selection, crossover and mutation. The apparatuses of index matrices and intuitionistic fuzzy sets, underlying the InterCriteria Analysis, are implemented to assess the performance of simple genetic algorithms for the parameter identification of Saccharomyces cerevisiae fed-batch fermentation process. The obtained results after the InterCriteria Analysis application are thoroughly analysed towards the algorithms outcomes, such as convergence time and model accuracy.

In this investigation, intuitionistic fuzzy logic is implemented to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Two kinds of simple genetic algorithms with operators sequences selection-crossover-mutation and mutation-crossover-selection are here considered, both applied for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. Intuitionistic fuzzy logic overbuilds the results achieved by the application of recently developed purposeful model parameters genesis procedure in order to keep promising results obtained. Behavior of applied algorithms has also been examined at different values of the genetic algorithms parameter generation gap, proven as the most sensitive parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for the assessment of algorithms performances have been compared and based on the evaluations in each case the most reliable algorithm has been distinguished.

In this investigation the InterCriteria Analysis (ICrA) approach is applied. The apparatuses of index matrices and intuitionistic fuzzy sets are at the core of ICrA. They are used to examine the influences of two main genetic algorithms (GA) parameters—the rates of crossover (xovr) and mutation (mutr). A series of parameter identification procedures for S. cerevisiae and E. coli fermentation process models is fulfilled. Twenty GA with different xovr and mutr values are applied. Relations between ICrA criteria—GA parameters and outcomes, on the one hand, and fermentation process model parameters, on the other hand, are investigated. The ICrA approach is applied by pairs, as well as by triples. The obtained results are thoroughly analysed towards computation time and model accuracy and some conclusions about the derived criteria interactions are reported.

© Springer International Publishing Switzerland 2016. Generalized nets (GN) are applied here to describe some basic operators of genetic algorithms, namely selection, crossover and mutation and different functions for selection (roulette wheel selection method and stochastic universal sampling), different crossover techniques (one-point crossover, two-point crossover, and “cut and splice” technique), as well as mutation operator (mutation operator of the Breeder genetic algorithm). The resulting GN models can be considered as separate modules, but they can also be accumulated into a single GN model to describe a whole genetic algorithm.

In this investigation recently developed InterCriteria Analysis (ICA) is applied aiming at examination of the influence of a genetic algorithm (GA) parameter in the procedure of a parameter identification of a fermentation process model. Proven as the most sensitive GA parameter, generation gap is in the focus of this investigation. The apparatuses of index matrices and intuitionistic fuzzy sets, laid in the ICA core, are implemented to establish the relations between investigated here generation gap, from one side, and model parameters of fed-batch fermentation process of Saccharomyces cerevisiae, from the other side. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about observed interactions are derived.

In this paper recently proposed procedure for purposeful model parameters genesis, originally developed for simple genetic algorithm, has been validated for multi-population genetic algorithm when it is applied for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. Proposed procedure aims to improve the algorithm effectiveness in respect to the convergence time and model accuracy treating intervals of variations of model parameters. Obtained results after the procedure application show more than 12% improvement of multi-population genetic algorithm convergence time while saving and even slightly meliorating the model accuracy.

Genetic algorithms, proved as successful alternative to conventional optimization methods for the purposes of parameter identification of fermentation process models, search for a global optimal solution via three main genetic operators, namely selection, crossover, and mutation. In order to determine their importance for finding the solution, a procedure for significance assessment of genetic algorithms operators has been developed. The workability of newly elaborated procedure has been tested when simple genetic algorithm is applied to parameter identification of S. cerevisiae fed-batch cultivation. According to obtained results the most significant genetic operator has been distinguished and its influence for finding the global optimal solution has been evaluated.

Intuitionistic fuzzy logic (IFL) has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of S. cerevisiae fed-batch cultivation model parameters obtained using standard simple (SGA) and multi-population (MpGA) genetic algorithms. Performances of MpGA have been tested before and after the application of the procedure for purposeful model parameters genesis at three different values of generation gap, proven as the most sensitive genetic algorithms parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for MpGA performances assessment have been compared and MpGA at GGAP = 0.1 after the purposeful model parameters genesis procedure application has been distinguished as the fastest and the most reliable one. Further, the prominent MpGA at GGAP = 0.1 has been compared to SGA at GGAP = 0.1. Obtained results have been assessed applying IFL and the most reliable algorithm has been distinguished.

The apparatus of Generalized Nets (GN) is applied here to describe different kinds of genetic algorithms (GA). Failure of conventional optimization methods to lead to a satisfied solution in parameter identification of non-linear and time-dependent parameters provokes an idea some stochastic algorithms to be applied. As such genetic algorithms (GA), as a promising metaheuristic technique, are widely used. Different modifications of simple genetic algorithms (SGA) have been investigated and successfully applied to parameter identification of fermentation processes aiming to improve the model accuracy and the algorithm convergence time. Altogether six modifications of SGA have been proposed with a different sequence of implementation of basic genetic operators selection, crossover and mutation. In the present GN model the user is allowed to choose the sequence of execution of main GA operators, thus resulting in one of the six considered here modifications of SGA. © 2012 IEEE.

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Different kinds of genetic algorithms have been investigated for a parameter identification of a fermentation process. Altogether eight realizations of genetic algorithms have been presented - four of simple genetic algorithms and four of multi-population ones. Each of them is characterized with a different sequence of implementation of main genetic operators, namely selection, crossover and mutation. A comparison of considered eight kinds of genetic algorithms is presented for a parameter identification of a fed-batch cultivation of S. cerevisiae. All kinds of multi-population algorithms lead to considerable improvement of the optimization criterion value but for more computational time. Among the considered multi-population algorithms, the best one has an operators' sequence of crossover, mutation and selection. Different kinds of considered simple genetic algorithms lead to similar values of the optimization criterion but the genetic algorithm with an operators' sequence of mutation, crossover and selection is significantly faster than the others.

The apparatus of Generalized Nets is applied here to a description of a selection operator, which is one of the basic genetic algorithm operators. This genetic operator performs a probabilistic selection based upon the individual's fittness such that the better individuals have an increased chance of being selected for the next generation. The resulting GN model could be considered as a separate module, but can also be accumulated into a GN model to describe a whole genetic algorithm.

The paper presents the implementation of multiple model approach to modelling of Escherichia coli BL21(DE3)pPhyt109 fed-batch cultivation processes for an extracellular production of bacterial phytase. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. Multiple model approach is an alternative concept which helps in modelling and control of complex processes. The main idea is the development of a model based on simple submodels for the purposes of further high quality process control. The presented simulations of E. coli fed-batch cultivation show how the process could be divided into different functional states and how the model parameters could be obtained easily using genetic algorithms. The obtained results and model verification demonstrate the effectiveness of the applied concept of multiple model approach and of the proposed identification scheme. © 2007 by Pontificia Universidad Católica de Valparaíso.

Genetic algorithms are widely applied bioinspired optimisation technique that search for a global optimal solution via three main genetic operators, namely selection, crossover, and mutation. In order to determine the operators importance when multi-population genetic algorithm is applied for parameter identification of S. cerevisiae fed-batch cultivation, recently presented procedure for significance assessment has been implemented. Based on obtained results the most significant genetic operator has been distinguished and its influence on finding the global optimal solution has been evaluated as well.

Simple genetic algorithms have been investigated aiming to improve the algorithm convergence time. Because of the stochastic nature of genetic algorithms, several runs have to be performed in order to achieve representative results. A procedure for purposeful genesis concerning intervals of variations of model parameters is proposed for a standard simple genetic algorithm, aiming to improve significantly the algorithm effectiveness. Such a stepwise methodology is applied to parameter identification of fed-batch cultivation of S. cerevisiae. The procedure is further validated to a modified simple genetic algorithm with changed sequence of main genetic algorithm operators, namely mutation, crossover and selection, proven to be faster than the standard one. Results obtained from both applications show significant improvement of the algorithm convergence time while saving the model accuracy.