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توضیحات

Platform for robust design optimization

ویژگی	مقدار
سیستم عامل	POSIX :: Linux
نام فایل	artap-2022.1.1.1759
نام	artap
نسخه کتابخانه	2022.1.1.1759
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Artap Team
ایمیل نویسنده	artap.framework@gmail.com
آدرس صفحه اصلی	http://www.agros2d.org/artap/
آدرس اینترنتی	https://pypi.org/project/artap/
مجوز	License :: OSI Approved :: MIT License

# 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. ### Linux You can install the full package, which contains the agrossuite package by the following command: pip install --upgrade pip # make sure that pip is reasonably new pip install artap[full] ## 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] Karban, Pavel, David Pánek, Tamás Orosz, Iveta Petrášová, and Ivo Doležel. “FEM based robust design optimization with Agros and Ārtap.” Computers & Mathematics with Applications (2020) https://doi.org/10.1016/j.camwa.2020.02.010. [2] Pánek, David, Tamás Orosz, and Pavel Karban. ” Ārtap: robust design optimization framework for engineering applications.” arXiv preprint arXiv:1912.11550 (2019). ### Applications [3] 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, https://doi.org/10.1108/COMPEL-08-2017-0356. [4] 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, https://doi.org/10.1109/PQ.2019.8818256. [5] Pánek, D., Karban, P., & Doležel, I. (2019). Calibration of Numerical Model of Magnetic Induction Brazing. IEEE Transactions on Magnetics, 55(6), 1-4, https://doi.org/10.1109/TMAG.2019.2897571. [6] Pánek, D., Orosz, T., Karban, P., & Doležel, I. (2020), “Comparison of simplified techniques for solving selected coupled electroheat problems”, COMPEL – The international journal for computation and mathematics in electrical and electronic engineering, Vol. 39 No. 1, pp. 220-230. https://doi.org/10.1108/COMPEL-06-2019-0244 [7] Orosz, T.; Pánek, D.; Karban, P. FEM Based Preliminary Design Optimization in Case of Large Power Transformers. Appl. Sci. 2020, 10, 1361, https://doi.org/10.3390/app10041361. ## Contact If you have any questions, do not hesitate to contact us: artap.framework@gmail.com ## License Ārtap is published under [MIT license](https://en.wikipedia.org/wiki/MIT_License)

نیازمندی

مقدار	نام
-	SALib
-	appdirs
-	cython
-	joblib
-	matplotlib
-	nlopt
-	numpy
>=1.13.3	numpy
-	numpydoc
-	pyDOE2
-	pymoo
-	rpyc
-	scikit-learn
-	scipy
-	setuptools
-	six
-	smt
-	tensorflow
-	wheel
>=0.01	agrossuite

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl artap-2022.1.1.1759:

pip install artap-2022.1.1.1759.whl

نصب پکیج tar.gz artap-2022.1.1.1759:

pip install artap-2022.1.1.1759.tar.gz