China
Africa
Explore chapters and articles related to this topic
*
Published in Juan Gabriel Segovia-Hernández, Fernando Israel Gómez-Castro, ®, 2017
Juan Gabriel Segovia-Hernández, Fernando Israel Gómez-Castro
Inside MATLAB, it is possible to find several toolboxes and packages. Regarding toolboxes, the optimization toolbox provides considerable information about solvers, previously written to find parameters that minimize or maximize objectives with or without constraints. The optimization toolbox offers some stochastic optimization algorithms, such as simulated annealing and genetic algorithms in both mono-objective and multi-objective optimization. It is quite simple to find the optimization toolbox. Figure 6.1 shows the exact location of this toolbox. Notice that all figures where MATLAB is used were obtained with MATLAB 2013b (8.2.0.701).
Topics on Nontraditional Mathematical Branches
Published in Dingyü Xue, YangQuan Chen, Scientific Computing with MATLAB®, 2018
There are several genetic algorithm toolboxes under MATLAB. The Global Optimization Toolbox (former name is Genetic Algorithm and Direct Search Toolbox, GADT) by MathWorks is the official toolbox, and its functions are updated in each release of MATLAB. Apart from that, the Genetic Algorithm Optimization Toolbox (GAOT) [9] developed by Christopher Houck, Jeffery Joines and Michael Kay, North Carolina State University and the GA Toolbox [8] written by Peter Fleming and Andrew Chipperfield of Sheffield University are among the most commonly used free toolboxes.
Dynamic Optimization in Drying
Published in Alex Martynenko, Andreas Bück, Intelligent Control in Drying, 2018
In addition to a genetic algorithm (ga), the MATLAB optimization toolbox offers a powerful optimization tool (fmincon) that switches between different algorithms. Excel offers, by installing the solver add-in, the opportunity to use three types of solvers. In our experience all algorithms are satisfactory; the only problem is ending in a local instead of a global minimum. Therefore, the result should always be checked by changing the control vector parameters used to start the optimization algorithm.
On pressurized feeding approach for effective control on working gap in ECDM
Published in Materials and Manufacturing Processes, 2018
Tarlochan Singh, Akshay Dvivedi
The genetic algorithm (GA) was employed to optimize the individual quality characteristics. The objective was to maximize the MRR and minimize the HOC and taper. This optimization was performed within the upper and lower limits of the input process parameters. This optimization was conducted using optimization toolbox of Matlab-2013 software. Regression analysis was used to determine the relationships between the process parameters (applied voltage, exerted pressure, pulse on time, and electrolyte concentration) and quality characteristics (MRR, HOC, and taper). The development of second-order regression models was done using Minitab V17 statistical analysis software. These regression models are given as follows:where Y1, Y2, and Y3 are MRR, HOC, and taper, respectively. X1, X2, X3, and X4 are applied voltage, exerted pressure, pulse on time, and electrolyte concentration, respectively.