A Multi-cores Parallel Artificial Bee Colony Optimization Algorithm Based on Fork/Join Framework

  • Jiuyuan HuoEmail author
  • Liqun Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)


There are lots of computationally intensive tasks in optimization process of Artificial Bee Colony (ABC) algorithm, which requires large CPU processing time. To improve optimization precision and performance of the ABC algorithm, a parallel Multi-cores Parallel ABC algorithm (MPABC) was proposed based on the Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee population’s diversity, improve the global convergence ability and avoid falling into the local optimum. The performance of the original serial ABC algorithm and the MPABC algorithm was analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve the speedup of 3.795 and the efficiency of 94.87% in solving complex problems. It can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process.


Parallel Artificial bee colony algorithm Fork/join framework 



This work is supported by National Nature Science Foundation of China (Grant No. 61462058), Gansu Province Science and Technology Program (No. 1606RJZA004) and Gansu Data Engineering and Technology Research Center for Resources and Environment.


  1. 1.
    Tavares, L.G., Lopes, H.S., Lima, C.R.E.: A study of topology in insular parallel genetic algorithms. In: World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 632–635. IEEE (2009)Google Scholar
  2. 2.
    Kennedy J., Eberhart R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Fan, Q.Q., Yan, X.F.: Self-adaptive differential evolution algorithm with discrete mutation control parameters. Expert Syst. Appl. 42, 1551–1572 (2015)CrossRefGoogle Scholar
  4. 4.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Vol. 200. Erciyes university, engineering faculty, computer engineering department (2005)Google Scholar
  5. 5.
    Zhu, X.P., Zhang, C., Yin, J.X.: Optimization of water diversion based on reservoir operating rules: analysis of the Biliu River reservoir. China J. Hydrol. Eng. 19, 411–421 (2014)CrossRefGoogle Scholar
  6. 6.
    Tu, K.Y., Liang, Z.C.: Parallel computation models of particle swarm optimization implemented by multiple threads. Expert Syst. Appl. 38, 5858–5866 (2011)CrossRefGoogle Scholar
  7. 7.
    Parpinelli, R.S., Benitez, C.M.V., Lopes, H.S.: Parallel approaches for the artificial bee colony algorithm. In: Panigrahi, B.K., Shi, Y., Lim, M.-H. (eds.) Handbook of Swarm Intelligence, pp. 329–345. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Konstantinos, E.P.: Parallel cooperative micro-particle swarm optimization: a master-slave model. Appl. Soft Comput. 12, 3552–3579 (2012)CrossRefGoogle Scholar
  9. 9.
    Gardner, M., McNabb, A., Seppi, K.: A speculative approach to parallelization in particle swarm optimization. Swarm Intell. 6(2), 77–116 (2012)CrossRefGoogle Scholar
  10. 10.
    Akancha, T., Afshar, M.A.: Implementation of parallel artificial bee colony algorithm on vehicle routing problem. Int. J. Adv. Res. Sci. Eng. (IJARSE) 2(5), 122–130 (2013)Google Scholar
  11. 11.
    Lea, D.: A Java fork/join framework. In: Proceedings of the ACM 2000 Conference on Java Grande, pp. 36–43. ACM, June 2000Google Scholar
  12. 12.
    Gao, W., Liu, S., Huang, L.: A global best artificial bee colony algorithm for global optimization. J. Comput. Appl. Math. 236(11), 2741–2753 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Alba, E., Luque, G.: Evaluation of parallel metaheuristics. In: Parallel Problem Solving from Nature (PPSN-EMAA 2006). LNCS, vol. 4193, pp. 9–14 (2006)Google Scholar
  14. 14.
    Karaboga, D., Akay, B., Ozturk, C.: Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS, vol. 4617, pp. 318–329. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73729-2_30 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhouPeople’s Republic of China
  2. 2.Gansu Data Engineering and Technology Research Center for Resources and EnvironmentLanzhouChina
  3. 3.College of Information Science and TechnologyGansu Agricultural UniversityLanzhouChina

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