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        "author": "Artap Team",
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        "description": "# \u0100rtap\n\n\u0100rtap 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.\n\n## Installation\n\nArtap and its dependencies are available as wheel packages for Windows and Linux* distributions:\nWe recommend to install Artap under a [virtual environment](https://docs.python.org/3/tutorial/venv.html).\n\n    pip install --upgrade pip # make sure that pip is reasonably new\n    pip install artap\n\n*The Windows versions are only partially, the linux packages are fully supported at the current version.\n\n## Basic usage\n\nThe goal of this example to show, how we can use Artap to solve a simple, bi-objective optimization problem.\n\nThe problem is defined in the following way [GDE3]:\n\n    Minimize f1 = x1\n    Minimize f2 = (1+x2) / x1\n\n    subject to\n            x1 e [0.1, 1]\n            x2 e [0, 5]\n\nThe 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.\n NSGA - II algorithm is used to solve this example.\n\n### The Problem definition and solution with NSGA-II in \u0100rtap:\n\n    class BiObjectiveTestProblem(Problem):\n\n        def set(self):\n\n            self.name = 'Biobjective Test Problem'\n\n            self.parameters = [{'name':'x_1', 'bounds': [0.1, 1.]},\n                               {'name':'x_2', 'bounds': [0.0, 5.0]}]\n\n            self.costs = [{'name': 'f_1', 'criteria': 'minimize'},\n                          {'name': 'f_2', 'criteria': 'minimize'}]\n\n        def evaluate(self, individual):\n            f1 = individual.vector[0]\n            f2 = (1+individual.vector[1])/individual.vector[0]\n        return [f1, f2]\n\n    # Perform the optimization iterating over 100 times on 100 individuals.\n    problem = BiObjectiveTestProblem()\n    algorithm = NSGAII(problem)\n    algorithm.options['max_population_number'] = 100\n    algorithm.options['max_population_size'] = 100\n    algorithm.run()\n\n## References\n\n* [GDE3] Saku Kukkonen, Jouni Lampinen, The third Evolution Step of Generalized Differential Evolution\n\n\n## Citing\n\nIf you use \u0100rtap in your research, the developers would be grateful if you would cite the relevant publications:\n\n[1] David P\u00e1nek, Tam\u00e1s 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\u20135, [in press]\n\n### Applications\n[2] Karban, P., P\u00e1nek, D., & Dole\u017eel, 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.\n\n[3] P\u00e1nek, D., Orosz, T., Krop\u00edk, P., Karban, P., & Dole\u017eel, 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.\n\n[4] P\u00e1nek, D., Karban, P., & Dole\u017eel, I. (2019). Calibration of Numerical Model of Magnetic Induction Brazing. IEEE Transactions on Magnetics, 55(6), 1-4.\n\n## Contact\n\nIf you have any questions, do not hesitate to contact us: artap.team@gmail.com\n\n## License\n\n\u0100rtap is published under [MIT license](https://en.wikipedia.org/wiki/MIT_License)\n\n\n",
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