Abstract
The paper discusses the use of an optimization algorithm based on the behaviour of the ant colony to solve the problem of forming the composition of a multiversion fault-tolerant software package. A model for constructing a graph for the implementation of the ant algorithm for the selected task is proposed. The modifications of the basic algorithm for both the ascending and the descending design styles of software systems are given. When optimizing for downstream design, cost, reliability, and evaluation of the successful implementation of each version with the specified characteristics are taken into account. When optimizing for up-stream design, reliability and resource intensity indicators are taken into account, as there is a selection from already implemented software modules. A method is proposed for increasing the efficiency of the ant algorithm, which consists in launching a group of “test” ants, choosing the best solution from this group and further calculating on the basis of it. A software system that implements both modifications of the basic ant algorithm for both design styles, as well as the possibility of applying the proposed multiple start technique to both modifications, is considered. The results of calculations obtained using the proposed software tool are considered. The results confirm the applicability of ant algorithms to the problem of forming a multiversion software package, and show the effectiveness of the proposed method.
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References
Dorigo, M., Birattari, M.: Swarm intelligence. Scholarpedia 2(9), 1462 (2007)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithm for discrete optimization. Artif. Life 5(2), 137–172 (1999)
Zhai, Y., Xu, L., Yang, Y.: Ant colony algorithm research based on pheromone update strategy. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 1, pp. 38–41 (2015)
Yin, M.-L., Peterson, J., Arellano, R.R.: Software complexity factor in software reliability assessment. In: Annual Symposium Reliability and Maintainability, pp. 190–194 (2004)
Fisher, M.S.: Software Verification and Validation: An Engineering and Scientific Approach. Springer, New York (2007). https://doi.org/10.1007/978-0-387-47939-2. 172 p.
Kovalev, I., Losev, V., Saramud, M., Petrosyan, M.: Model implementation of the simulation environment of voting algorithms, as a dynamic system for increasing the reliability of the control complex of autonomous unmanned objects. In: MATEC Web of Conferences, 31 October 2017, vol. 132, no. 04011 (2017)
Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. BioSystems 43(2), 73–81 (1997)
Yang, X., Wang, J.-S.: Application of improved ant colony optimization algorithm on traveling salesman problem. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 2156–2160 (2016)
Saramud, M.V., Kovalev, I.V., Losev, V.V., Karaseva, M.V., Kovalev, D.I.: On the application of a modified ant algorithm to optimize the structure of a multiversion software package. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10941, pp. 91–100. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93815-8_10
Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344, 243–278 (2005)
Acknowledgments
This work was supported by Ministry of Education and Science of Russian Federation within limits of state contract № 2.2867.2017/4.6
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Saramud, M.V., Kovalev, I.V., Losev, V.V., Voroshilova, A.A. (2019). Multiple Start Modifications of Ant Colony Algorithm for Multiversion Software Design. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_18
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