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.
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The first author would like to thank the Ministry of Higher Education and Scientific Research (MOHESR), Iraq.
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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
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