Maria Semenkina
ABSTRACT
Technological and command-programming control contours of a spacecraft are modelled with Markov chains. These models are used for the preliminary design of spacecraft control system effective structure with the use of special DSS. Corresponding optimization problems with algorithmically given functions of mixed variables are solved with a special stochastic algorithm called self-configuring genetic algorithm that requires no settings determination and parameter tuning. The high performance of the suggested algorithm is proved by solving real problems of the control contours structure preliminary design.
- Semenkin, E., Semenkina, M. 2012. Spacecrafts' Control Systems Effective Variants Choice with Self-Configuring Genetic Algorithm. In: Ferrier, J.-L., Bernard, A., Gusikhin, O. and Madani, K. (eds), Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, volume 1, 84--93.Google Scholar
- Eiben, A.E., Hinterding, R., and Michalewicz, Z. 1999. Parameter control in evolutionary algorithms. In: IEEE Transactions on evolutionary computation, 3(2). IEEE Press Piscataway, NJ, USA, 124--141. Google ScholarDigital Library
- Semenkin, E., Semenkina, M. 2012. Self-Configuring Genetic Algorithm with Modified Uniform Crossover Operator. In: Tan, Y., Shi, Y., and Ji, Z. (Eds.): Advances in Swarm Intelligence, ICSI 2012, Part I, LNCS 7331. Springer, Heidelberg, 414--421. Google ScholarDigital Library
- Lardeux, F., Goëffon, A. 2010. A Dynamic Island-Based Genetic Algorithms Framework. In: Simulated Evolution and Learning, Lecture Notes in Computer Science 6457. Springer, Heidelberg, 156--165. Google ScholarDigital Library
- Finck, S., Hansen, N., Ros, R., and Auger, A. 2009. Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE.Google Scholar
Index Terms
- Parallel version of self-configuring genetic algorithm application in spacecraft control system design
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