Abstract
In this work, a new efficient evolutionary algorithm to enhance the global optimization search is presented, which applies double populations, each population divided into several groups. The first population is original and the second one is a copy of the first one but with different operators are applied to it. The operators used in this paper are dynamic schema, dynamic dissimilarity, dissimilarity, similarity and a random generation of new chromosomes. This algorithm is called Multi-Dynamic Schema with Dissimilarity and Similarity of Chromosomes (MDSDSC) which is a more elaborate version of our previous DSC and DSDSC algorithms. We have applied this algorithm to 20 test functions in 2 and 10 dimensions. Comparing the MDSDSC with the classical GA, DSC, DSDSC and, for some functions, BA and PSO algorithms, we have found that, in most cases, our method is better than the GA, BA and DSC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wu, Y., Sun, G., Su, K., Liu, L., Zhang, H., Chen, B., Li, M.: Dynamic self-adaptive double population particle swarm optimization algorithm based on Lorenz equation. J. Comput. Commun. 5(13), 9–20 (2017)
Park, T., Ryu, K.R.: A dual-population genetic algorithm for adaptive diversity control. IEEE Trans. Evol. Comput. 14(6), 865–884 (2010)
Al-Jawadi, R.: An optimization algorithm based on dynamic schema with dissimilarities and similarities of chromosomes. Int. J. Comput. Electr. Autom. Control Inf. Eng. 7(8), 1278–1285 (2016)
Al-Jawadi, R., Studniarski, M., Younus, A.: A new genetic algorithm based on dissimilarities and similarities. Comput. Sci. J. 19(1), 19 (2018)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence, 3rd edn. Springer, Heidelberg (1996). https://doi.org/10.1007/978-3-662-03315-9
Sultan, A.B.M., Mahmod, R., Sulaiman, M.N., Abu Bakar, M.R.: Maintaining diversity for genetic algorithm: a case of timetabling problem. J. Teknol. Malaysia 44(5), 123–130 (2006)
Eesa, A.S., Brifcani, A.M.A., Orman, Z.: A new tool for global optimization problems- Cuttlefish algorithm. Int. J. Comput. Electr. Autom. Control Inf. Eng. 8(9), 1198–1202 (2014)
Ritthipakdee, A., Thammano, A., Premasathian, N., Uyyanonvara, B.: An improved firefly algorithm for optimization problems. In: ADCONP, Hiroshima, no. 2, pp. 159–164 (2014)
Iqbal, M.A., Khan, N.K., Mujtaba, H., Baig, A.R.: A novel function optimization approach using opposition based genetic algorithm with gene excitation. Int. J. Innov. Comput. Inf. Control 7(7), 4263–4276 (2011)
Odili, J.B., Nizam, M., Kahar, M.: Numerical function optimization solutions using the African buffalo optimization algorithm (ABO). Br. J. Math. Comput. Sci. 10(1), 1–12 (2015)
Scott, E.O., De Jong, K.A.: Understanding simple asynchronous evolutionary algorithms. In: United Kingdom ACM FOGA 2015, Aberystwyth, 17–20 January 2015
Acknowledgments
The first author would like to thank the Ministry of Higher Education and Scientific Research (MOHESR), Iraq.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Al-Jawadi, R., Studniarski, M. (2018). An Optimization Algorithm Based on Multi-Dynamic Schema of Chromosomes. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-91253-0_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91252-3
Online ISBN: 978-3-319-91253-0
eBook Packages: Computer ScienceComputer Science (R0)