Interlock Optimization Of An Accelerator Using Genetic Algorithm

Abstract

Accelerators are systems where high-tech experiments are conducted today and contain high-tech constructions. Construction and operation of accelerators require multidisciplinary studies. Each accelerator structure has its own characteristics as well as similar features of accelerator structures. Control systems come to the forefront as one of the most important structures that make up accelerators. Since control systems have critical importance for accelerators, in such systems when a problem occurs, there is a danger of environmental and human safety as well as machine system. For that reason interlock systems are being developed in different structures. In the literature, FPGAs and PLCs in such interlock systems have been shown to be suitable for use in accelerators [1,7].

In this work, we describe an interlock system that evaluates the operation and protection modes of devices used in an electron accelerator. In order to ensure that this system can operate at minimum cost and maximum safety, the defined system is divided into 3 subsystems. The error messages from the control devices in the accelerator control systems are the input to the interlock system. The purpose of the interlock system that evaluates error messages is to ensure that the accelerator closes safely.

The purpose of this study is to specify which of the 3 interlock subsystems which are defined for minimum cost and maximum security should be connected to the fault outputs from the control devices. As an evaluation criterion, 6 features are defined for the control devices and each control device is weighted according to the importance of the task. In the solution of the problem, genetic algorithms were used for assigning 74 controller outputs to 3 interlock subsystems. Thanks to the Genetic Algorithm used in the study, 94.3% success rate was obtained in terms of cost and safe system.

Keywords

• Optimization,Genetic Algorithm,Accelerator

References

1. M. Kago, T. Matsushita, N. Nariyama, C. Saji, R. Tanaka, A. Yamashita, Y. Asano, T. Fukui, T. Itoga, System Design of Accelerator Safety Interlock for the XFEL/SPRING-8, Proceedings of IPAC’10, Kyoto, Japan
2. Charu C. Aggarwal, Data Mining and Knowledge Discovery Series, CRC Press, 2015
3. Melanie Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1999
4. Tan KC, Tay A, Lee TH, Heng CM. Mining multiple comprehensible classification rules using genetic programming. In: IEEE Congress on Evolutionary Computation, Honolulu, HI, 2002. p. 1302–7.
5. J. R. Koza. Genetic Programming: on the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992
6. Sivanandam S. N. Deepa S. N. Introduction to Genetic Algorithms Springer-Verlag, Berlin, Heidelberg, 2008
7. R. Schmidt Machine Protection and Interlock Systems for Circular Machines—Example for LHC CERN, Geneva, Switzerland arXiv:1608.03087v1 [physics.acc-ph] 10 Aug 2016
8. Sankar Kumar Pal Classification and learning using genetic algorithms_ applications in bioinformatics and web intelligence-Springer (2007)

9. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York, 1989.
10. C. C Bojarczuk, H.S. Lopes, and A.A. Freitas. Genetic Programming for Knowledge Discovery in Chest-Pain Diagnosis. IEEE Engineering in Medicine and Biology, July/August, pp. 38-44,2000
11. Miller, Brad; Goldberg, David (1995). "Genetic Algorithms, Tournament Selection, and the Effects of Noise". Complex Systems. 9: 193–212
12. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. CA: Morgan Kaufmann Publishers, 1999.