ACM Transactions on Mathematical Software (TOMS)
Journal of Global Optimization
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Stability conditions of fuzzy systems and its application to structural and mechanical systems
Advances in Engineering Software
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
The stability of an oceanic structure with T-S fuzzy models
Mathematics and Computers in Simulation
Designing a model of FANP in brand image decision-making
Applied Soft Computing
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
GA-based decoupled adaptive FSMC for nonlinear systems by a singular perturbation scheme
Neural Computing and Applications
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Optimization of fed-batch fermentation processes with bio-inspired algorithms
Expert Systems with Applications: An International Journal
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Mathematical models in biochemical engineering field are usually composed by nonlinear kinetic equations, where the number of parameters that must be estimated from a set of experimental measurements is usually very high. In these cases, the estimation of the model parameters comprises numerical iterative methods for minimization of the objective function. Classical methods for minimization of the objective function, like the Newton method, requires a good initial guess for all parameters and differentiation of the objective function and/or model equations with respect to the model parameters. Besides, the use of stochastic optimization methods for parameter estimation has gained attention, since these methods do not require a good initial guesses of all model parameters and neither the evaluation of derivatives. In this work, some stochastic optimization methods (Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization and Simulated Annealing) were used in the estimation of kinetic parameters of a biochemical model for an alcoholic fermentation of cassava hydrolyzed. The results indicated that Differential Evolution provides better results among the stochastic optimization methods evaluated.