Metadata-Version: 2.1
Name: artap
Version: 0.1.11
Summary: Platform for robust design optimization
Home-page: http://www.agros2d.org/artap/
Author: Artap Team
Author-email: artap.team@gmail.com
License: License :: OSI Approved :: MIT License
Description: # Ārtap
        
        Ārtap is a framework for robust design optimization in Python. It contains an integrated, multi-physical FEM solver: Agros suite, furthermore it provides simple interfaces for commercial FEM solvers (COMSOL) and meta-heuristic, bayesian or neural network based optimization algorithms surrogate modelling techniques and neural networks.
        
        ## Installation
        
        Artap and its dependencies are available as wheel packages for Windows and Linux* distributions:
        We recommend to install Artap under a [virtual environment](https://docs.python.org/3/tutorial/venv.html).
        
            pip install --upgrade pip # make sure that pip is reasonably new
            pip install artap
        
        *The Windows versions are only partially, the linux packages are fully supported at the current version.
        
        ## Basic usage
        
        The goal of this example to show, how we can use Artap to solve a simple, bi-objective optimization problem.
        
        The problem is defined in the following way [GDE3]:
        
            Minimize f1 = x1
            Minimize f2 = (1+x2) / x1
        
            subject to
                    x1 e [0.1, 1]
                    x2 e [0, 5]
        
        The Pareto - front of the following problem is known, it is a simple hyperbola. This problem is very simple for an Evolutionary algorithm, it finds its solution within 20-30 generations.
         NSGA - II algorithm is used to solve this example.
        
        ### The Problem definition and solution with NSGA-II in Ārtap:
        
            class BiObjectiveTestProblem(Problem):
        
                def set(self):
        
                    self.name = 'Biobjective Test Problem'
                    
                    self.parameters = [{'name':'x_1', 'bounds': [0.1, 1.]},
                                       {'name':'x_2', 'bounds': [0.0, 5.0]}]
        
                    self.costs = [{'name': 'f_1', 'criteria': 'minimize'},
                                  {'name': 'f_2', 'criteria': 'minimize'}]
        
                def evaluate(self, individual):
                    f1 = individual.vector[0]
                    f2 = (1+individual.vector[1])/individual.vector[0]
                return [f1, f2]
         
            # Perform the optimization iterating over 100 times on 100 individuals.
            problem = BiObjectiveTestProblem()
            algorithm = NSGAII(problem)
            algorithm.options['max_population_number'] = 100
            algorithm.options['max_population_size'] = 100
            algorithm.run()
        
        ## References
        
        * [GDE3] Saku Kukkonen, Jouni Lampinen, The third Evolution Step of Generalized Differential Evolution
        
        
        ## Citing
        
        If you use Ārtap in your research, the developers would be grateful if you would cite the relevant publications:
        
        [1] David Pánek, Tamás Orosz, Pavel Karban, Artap: Robust design optimization framework for engineering applications, in: The Third International Conference on Intelligent Computing in Data Sciences ICDS2019, IEEE, 2019, pp. 1–5, [in press]
        
        ### Applications
        [2] Karban, P., Pánek, D., & Doležel, I. (2018). Model of induction brazing of nonmagnetic metals using model order reduction approach. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering, 37(4), 1515-1524.
        
        [3] Pánek, D., Orosz, T., Kropík, P., Karban, P., & Doležel, I. (2019, June). Reduced-Order Model Based Temperature Control of Induction Brazing Process. In 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM) (pp. 1-4). IEEE.
        
        [4] Pánek, D., Karban, P., & Doležel, I. (2019). Calibration of Numerical Model of Magnetic Induction Brazing. IEEE Transactions on Magnetics, 55(6), 1-4.
        
        ## Contact
        
        If you have any questions, do not hesitate to contact us: artap.team@gmail.com
        
        ## License
        
        Ārtap is published under [MIT license](https://en.wikipedia.org/wiki/MIT_License)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >3.6
Description-Content-Type: text/markdown
