1. bookVolume 114 (2017): Issue 9 (September 2017)
Journal Details
License
Format
Journal
eISSN
2353-737X
First Published
20 May 2020
Publication timeframe
1 time per year
Languages
English
access type Open Access

Multi-thread evolutionary computation for design optimization

Published Online: 29 May 2020
Volume & Issue: Volume 114 (2017) - Issue 9 (September 2017)
Page range: 197 - 206
Journal Details
License
Format
Journal
eISSN
2353-737X
First Published
20 May 2020
Publication timeframe
1 time per year
Languages
English
Abstract

The paper presents multi-thread calculations using parallel evolutionary algorithms (EA) for single and multicriteria design optimization. This approach was implemented to avoid a negative influence of incorrectly chosen initial and EA’s control parameters for the accuracy of generated solutions and thereby to improve the effectiveness of the EA’s use. Parallel computation for single optimization problems relies just on running n threads with different randomly chosen parameters in order to find the best final solution. For multicriteria optimization problems, each thread generates a set of Pareto optimal solutions and at the end these sets are combined together, giving a real set of Pareto optimal solutions. During the run of the algorithm, random interactions between threads were applied. The experiments were carried out using ten-thread processes for different examples of single and multicriteria design optimization problems, two of which are presented in the paper.

Keywords

[1] Burczynski T., Dlugosz A., Kus W., Parallel Evolutionary Algorithms in Shape Optimization of Heat Radiators, Journal of Theoretical and Applied Mechanics 44, 2, Warszawa 2006, 351–366.Search in Google Scholar

[2] Grefenstette J., Optimization of Control Parameters for Genetic Algorithms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 16, No. 1, 1986, 122–128.10.1109/TSMC.1986.289288Search in Google Scholar

[3] Kieś P., Selection of genetic algorithm parameters using off-line method(In Polish), Instytut Naukowo-Badawczy ZTUREK, Warszawa 2000.Search in Google Scholar

[4] Krenich S., Genetic Algorithms in Parametrical Optimization of Robot Gripper Mechanisms, Ph.D. thesis (in Polish), Wydział Mechaniczny, Politechnika Krakowska, Kraków 2002.Search in Google Scholar

[5] Lis J., Lis M., Self-adapting Parallel Genetic Algorithm with Dynamic Mutation Probability, Crossover Rate and Population Size, Proceedings of the First Polish National Conference on Evolutionary Computing, J. Arabas (ed.), Politechnika Warszawska, Warszawa 1996, 324–329.Search in Google Scholar

[6] Miki M., Hiroyasu T., Hatanaka K., Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme, The 3rd World Congress on Structural and Multidisciplinary Optimization, Buffalo 17–22 May, USA, 1999.Search in Google Scholar

[7] Osyczka A., Evolutionary Algorithms for Single and Multicriteria Design Optimization, Springer-Verlag Physica, Berlin Heilderberg, 2002.Search in Google Scholar

[8] Osyczka A., Krenich S., Evolutionary Algorithms for Global Optimization, [in:] Global Optimization – Selected Case Studies, J. Pinter (ed.), Kluwer Academic Publishers, Dordrecht/Boston/London 2007.Search in Google Scholar

[9] Osmera P., Lacko B., Peter M., Parallel Evolutionary Algorithms, Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation, 2003.Search in Google Scholar

[10] Sadecki J., Parallel algorithms for optimization and testing of their effectiveness (in Polish), Oficyna Wydawnicza Politechniki Opolskiej, Opole 2001.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo