It seems that due to the complexity of the objective function and also uncertain parameters, the linear regression is not the best algorithm for optimization.
In order to choose the best metamodel for optimization, the accuracies of these four kinds of metamodels are compared by employing the error evaluation indicators of the relative error (RE) and the root mean square error (RMSE).
In order to determine the optimum number of hidden nodes, a series of different topologies was used, in which the nodes were varied from 1 to 8. The RBF of the lowest error constructed in this manner was then used as the best model for optimization of nanoparticle preparation process.
Although RSM is probably the best approach for optimization of biological processes, the IF-STT is faster, it examines the most important factors (bacterial strain, temperature and time) influencing protein soluble expression in a single experiment, and can be used in any recombinant protein expression project as a starting point.
This simple cycle is the basic step in the GA because repeating this procedure and according to supposed criteria, the best answer for optimization problem would be derived.
The best models for optimization of culture parameters were achieved by multilayer full feedforward incremental back propagation network and modified response surface model using backward elimination, where the optimum condition was: growth temperature (52.3°C), medium volume (50 ml), inoculum size (1%), agitation rate (static condition), incubation period (24 h) and initial pH (5.8).
We leave the task of identifying the best possible algorithm for optimization to future work.
The procedure covered the 5-dimensional parameter space by a grid so as to find the best starting point for optimization.
Then, the best algorithm in these simulations will be selected as the best single objective optimization algorithms for optimization problems in power systems.
DE is one of the best-performing evolutionary algorithms frequently used for optimization tasks, which often results in the optimal solution in shorter steps as compared to other optimization algorithms.
