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The algorithm of artificial immune system simulation with saaty selection operatorand one-dimensional local search

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Category: Information technologies, systems analysis and administration

Last Updated on 19 November 2016

Published on 17 November 2016

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Authors:

T.A.Zheldak, Cand. Sc. (Tech.), Assoc. Prof., State Higher Educational Institution “National Mining University”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Slesarev, Dr. Sc. (Tech.), Prof., State Higher Educational Institution “National Mining University”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.G.Gulina, PhD, State Higher Educational Institution “National Mining University”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.">This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract:

Purpose. Development of an algorithm which implements a certain method of modelling an artificial immune system for solving the task of multidimensional constrained optimization of multiextremum continuous functions and which provides increasing performance indicators.

Methodology. A hybrid adaptive immune algorithm is proposed. It uses an operator of clonal selection based on the evaluation of fitness of solutions using the method of Saaty’s hierarchy; pair adaptive crossover; the adaptive mutation based on polynomial and normal distribution laws; limited coordinate wise local search using the method the Golden section.

Findings. The use of the algorithm for multidimensional constrained optimization that simulates the behavior of the artificial immune system as the operator of selection is mathematically justified by the operator based on the method of analysis of Saaty’s hierarchies. It is proposed for the first time. Unlike any of known implementations of heuristic operators, it ensures a high precision and speed of the ascent up to the same level of problems.

Originality. The results show high efficiency of the proposed algorithm for optimization of standard objective functions used as a test on the spacial dimensions up to 100 iterations. The algorithm shows stable convergence and a higher speed when there is a task of training neural networks of direct distribution.

Practical value. The main advantages of the proposed algorithm lie in the fact that it remains effective with the growth of the of the problem; it finds not one solution, but many of them (alternatives); use much less time for a comparable solution. These properties allow the application of the proposed algorithm to solve multi-criteria multi-factor optimization problems of decision making in the processes of complex systems control.

References/Список літератури

1. Lucinska, M., Wierzchon, S. T., 2009. Hybrid Immune Algorithm for Multimodal Function Optimization. Recent Advances in Intelligent Information Systems, Vol. 30, pp. 301–313.

2. Yildiz, A. R., 2009. A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, Vol. 25, pp. 261–270.

3. Brownlee, J., 2007. Clonal Selection Algorithms Technical Report 070209A. Complex Intelligent Systems Laboratory (CIS), Centre for Information Technology Research (CITR), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology.

4. Kelsey, J. and Timmis, J., 2003. Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation. [online]. Available at: http://www.cs.york.ac.uk/rts/docs/GECCO_2003/papers/ 2723/27230207.pdf.

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7. F. O. de França, F. J. V. Zuben and L. N. de Castro, 2005. An artificial immune network for multimodal function optimization on dynamic environments. In: Proc. Of GECCO, ACM Press, New York, pp. 289–296.

8. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, 2008. Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, Vol. 16(2), pp. 225–255.

9. Snytyuk, V. Y., 2012. Aimed optimization features and evolutionary generation of potential solutions. In: VI Mizhnarodna shkola-seminar Uzhgorod 1–6 October 2012, Uzhgorod: Invizor.

Снитюк В. Є. Спрямована оптимізація і особливості еволюційної генерації потенційних розв’язків: VI міжнародна школа-семінар „Теорія прийняття рішень“ (Ужгород 1–6 жовтня 2012 р.) / В. Є. Снитюк. – Ужгород: „Інвізор“, 2012. – C. 182–183.

10. Zheldak, T. A., 2013. Method of simulation of artificial immune system in multimodal function optimization problems. Computational intelligence (results, problems and prospects). In: Proceedings of the 2nd International scientific and technical conference. Cherkassy, 14–17 May 2013.

Желдак Т. А. Метод моделювання штучної імунної системи в задачах оптимізації мультимодальних функцій: матеріали 2-ї міжнар. наук.-техн. конф. „Обчислювальний інтелект (результати, проблеми, перспективи)“ (Черкаси 14–17 травня 2013 р.) / Т. А. Желдак. – 2013. – С. 33–36.

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