Abstract
This paper deals with a new method for decomposing search domain in a global optimization problem. Proposed method was designed for parallel population algorithms but also can be used as a diversification tool in sequential algorithms. New decomposition technique was compared with a traditional approach by means of numeric experiments with a use of multi-dimensional benchmark optimization functions and Mind Evolutionary Computation algorithm. Results of the experiments demonstrate the superiority of new technique over a canonical approach which resulted in a higher quality of obtained solutions.
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References
Weise, T.: Global Optimization Algorithms - Theory and Application. University of Kassel, 758 p. (2008)
Karpenko, A.P.: Modern algorithms of search engine optimization. In: Nature-Inspired Optimization Algorithms, 446 p. Bauman MSTU Publication, Moscow (2014). (in Russian)
Vorobeva, E.Y., Karpenko, A.P., Seliverstov, E.Y.: Co-evolutionary algorithm of global optimization based on particle swarm optimization. Sci. Educ. Bauman MSTU. (4), 431–474 (2012). doi:10.1007/1113.0619595
Sakharov, M.K., Karpenko, A.P., Velisevich, Y.I.: Multi-memetic mind evolutionary computation algorithm for loosely coupled systems of desktop computers. Sci. Educ. Bauman MSTU. (10), 438–452 (2015). doi:10.7463/1015.0814435
Karpenko, A.P., Sakharov, M.K.: Multi-memes global optimization based on the algorithm of mind evolutionary computation. Inf. Technol. 14(7), 23–30 (2014). (in Russian)
Sakharov, M., Karpenko, A.: New parallel multi-memetic MEC-based algorithm for loosely coupled systems. In: Proceedings of the VII International Conference on Optimization Methods and Application “Optimization and applications” (OPTIMA-2016), pp. 124–126 (2016)
Chengyi, S., Yan, S., Wanzhen, W.: A survey of MEC: 1998–2001. In: 2002 IEEE International Conference on Systems, Man and Cybernetics IEEE (SMC 2002), Hammamet, 6–9 October, vol. 6, pp. 445–453. Institute of Electrical and Electronics Engineers Inc. (2002)
Jie, J., Zeng, J.: Improved mind evolutionary computation for optimizations. In: Proceedings of 5th World Congress on Intelligent Control and Automation, Hangzhou, pp. 2200–2204 (2004)
Jie, J., Han, C., Zeng, J.: An extended mind evolutionary computation model for optimizations. Appl. Math. Comput. 185, 1038–1049 (2007)
Sakharov, M., Karpenko, A.: Performance investigation of mind evolutionary computation algorithm and some of its modifications. In: Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2016), pp. 475–486. Springer (2016). doi:10.1007/978-3-319-33609-1_43
Sakharov, M.K.: Study on mind evolutionary computation. In: Technologies and Systems 2014, pp. 75–78. Bauman MSTU Publishing, Moscow (2014)
Floudas, A.A., Pardalos, P.M., Adjiman, C., Esposito, W.R., Gümüs, Z.H., Harding, S.T., Klepeis, J.L., Meyer, C.A., Schweiger, C.A.: Handbook of Test Problems in Local and Global Optimization, 441 p. Kluwer, Dordrecht (1999)
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Sakharov, M., Karpenko, A. (2018). A New Way of Decomposing Search Domain in a Global Optimization Problem. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-68321-8_41
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DOI: https://doi.org/10.1007/978-3-319-68321-8_41
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