An Optimization Algorithm Based on Multi-Dynamic Schema of Chromosomes

  • Radhwan Al-JawadiEmail author
  • Marcin Studniarski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


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.


Dynamic schema Dissimilarity and Similarity of Chromosomes Double population 



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


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Park, T., Ryu, K.R.: A dual-population genetic algorithm for adaptive diversity control. IEEE Trans. Evol. Comput. 14(6), 865–884 (2010)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Al-Jawadi, R., Studniarski, M., Younus, A.: A new genetic algorithm based on dissimilarities and similarities. Comput. Sci. J. 19(1), 19 (2018)Google Scholar
  5. 5.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence, 3rd edn. Springer, Heidelberg (1996). Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Ritthipakdee, A., Thammano, A., Premasathian, N., Uyyanonvara, B.: An improved firefly algorithm for optimization problems. In: ADCONP, Hiroshima, no. 2, pp. 159–164 (2014)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Scott, E.O., De Jong, K.A.: Understanding simple asynchronous evolutionary algorithms. In: United Kingdom ACM FOGA 2015, Aberystwyth, 17–20 January 2015Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Mathematics, Informatics and MechanicsUniversity of WarsawWarsawPoland
  2. 2.Technical College of MosulMosulIraq
  3. 3.Faculty of Mathematics and Computer ScienceUniversity of ŁódźŁódźPoland

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