Review on Elephant Herding Optimization Algorithm Performance in Solving Optimization Problems

  • Authors

    • Saman M. Almufti computer science
    • Renas R. Asaad computer science
    • Baraa W. Salim computer science
    2019-05-11
    https://doi.org/10.14419/ijet.v7i4.28473
  • Metaheuristic, Elephant Herding Optimization (EHO), Optimization, Evolutionary Algorithms, Swarm Intelligences.

  • Abstract

    Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimization problems. EHO can be used to solve as benchmark problems, Services Selection in QoS-Aware Web Service Compositions, Energy-Based Localization, PID controller tuning, Appliance Scheduling in Smart Grid identification and other problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch (Female elephant), while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.

    In this paper, a review of the Elephant Herding optimization algorithm is presented. Moreover, a comparison of results of EHO compared to other optimization algorithm is presented based on previous work results. In the experimental results section, the result of EHO will be compared with the U-Turning Ant Colony Optimization Algorithm (U-TACO) in solving Traveling Salesman Problem (TSP), which is based on Ant Colony Optimization (ACO).

     

     

  • References

    1. [1] X. Yang, “Metaheuristic Optimization†Scholarpedia, 6(8), p.11472, 2011. https://doi.org/10.4249/scholarpedia.11472.

      [2] S. Almufti, "U-Turning Ant Colony Algorithm powered by Great Deluge Algorithm for the solution of TSP Problem", Hdl.handle.net, 2018. [Online].

      [3] S. Almufti, “Using Swarm Intelligence for solving NPHard Problems,†Academic Journal of Nawroz University, vol. 6, no. 3, pp. 46–50, 2017. https://doi.org/10.25007/ajnu.v6n3a78.

      [4] S. Almufti and A. Shaban, "U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem", Academic Journal of Nawroz University, vol. 7, no. 4, pp. 45-49, 2018. https://doi.org/10.25007/ajnu.v6n4a270.

      [5] J. Rajpurohit, T. Sharma and A. Abraham, "Glossary of MetaheuristicAlgorithms", International Journal of Computer Information Systems and Industrial Management Applications, vol. 9, pp. 181-205, 2017.

      [6] R. Asaad and N. Abdulnabi, "Using Local Searches Algorithms with Ant Colony Optimization for the Solution of TSP Problems", Academic Journal of Nawroz University, vol. 7, no. 3, pp. 1-6, 2018. https://doi.org/10.25007/ajnu.v7n3a193.

      [7] S. Chibani and A. Tari,†Elephant Herding Optimization for Service Selection in QoS-Aware Webâ€, International Journal of Computer and Information Engineering, 2017.

      [8] Y. Li, “Solving TSP by an ACO-and-BOA-based Hybrid Algorithmâ€. International Conference on Computer Application and System Modeling, pp. 189–192. IEEE Press,New York ,2010.

      [9] M. Dorigo,†Optimization, Learning and Natural Algorithmsâ€, PhD thesis, Politecnico di Milano, Italy, 1992.

      [10] C. Kahraman and G. Kayakutlu, Energy management - collective and computational intelligence with theory and applications. Cham, Switzerland: Springer, 2018. https://doi.org/10.1007/978-3-319-75690-5.

      [11] G. Wang, L. Dos Santos Coelho, X. Gao and S. Deb, "A new metaheuristic optimisation algorithm motivated by elephant herding behaviour", International Journal of Bio-Inspired Computation, vol. 8, no. 6, p. 394, 2016. https://doi.org/10.1504/IJBIC.2016.10002274.

      [12] G. Wang, S. Deb and L. Coelho, "Elephant Herding Optimization", 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), 2015. https://doi.org/10.1109/ISCBI.2015.8.

      [13] G. Wang, S. Deb, X. Zhao and Z. Cui, "A new monarch butterfly optimization with an improved crossover operator", Operational Research, vol. 18, no. 3, pp. 731-755, 2016. https://doi.org/10.1007/s12351-016-0251-z.

      [14] L. Barolli, F. Xhafa and J. Conesa, Advances on Broad-Band Wireless Computing, Communication and Applications. New York: Springer, 2017. https://doi.org/10.1007/978-3-319-49106-6.

      [15] P. Suganthan, N. Hansen, J. Liang, K. Deb, Y. Chen, A. Auger and S. Tiwari, “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimizationâ€, Nanyang Technological University, Singapore, 2005. https://doi.org/10.1109/TSE.2004.11.

      [16] L. Zeng, B. Benatallah, A. Ngu, M. Dumas, J. Kalagnanam and H. Chang, "QoS-aware middleware for Web services composition", IEEE Transactions on Software Engineering, vol. 30, no. 5, pp. 311-327, 2004. https://doi.org/10.1007/s00158-003-0368-6.

      [17] R. Marler and J. Arora, “Survey of multi-objective optimization methods for engineeringâ€. International journal of Struct. Multidiscipl. Optim, 2004.

      [18] S. Kalepu, S. Krishnaswamy and Seng Wai Loke, "Verity: a QoS metric for selecting web services and providers", Fourth International Conference on Web Information Systems Engineering Workshops, 2003. Proceedings. Available: 10.1109/wisew.2003.1286795.

      [19] L. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam and Q. Sheng, “Quality Driven Web Services Compositionâ€, In Twelfth International Conference of WWW, May 20-24, Budapest, 2003. https://doi.org/10.1145/775209.775211.

      [20] A. Lemos, F. Daniel and B. Benatallah, "Web Service Composition", ACM Computing Surveys, vol. 48, no. 3, pp. 1-41, 2015. https://doi.org/10.1145/2831270.

      [21] E. Al-Masri and Q. Mahmoud, "QoS-based Discovery and Ranking of Web Services", 2007 16th International Conference on Computer Communications and Networks, 2007. https://doi.org/10.1109/ICCCN.2007.4317873.

      [22] I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "Wireless sensor networks: a survey", Computer Networks, vol. 38, no. 4, pp. 393-422, 2002. https://doi.org/10.1016/S1389-1286(01)00302-4.

      [23] I. Strumberger, M. Beko, M. Tuba, M. Minovic and N. Bacanin, “Elephant Herding Optimization Algorithm for Wireless Sensor Network Localization Problemâ€. IFIP Advances in Information and Communication Technology, pp.175-184, 2018. https://doi.org/10.1007/978-3-319-78574-5_17.

      [24] I. Strumberger, E. Tuba, N. Bacanin, M. Beko and M. Tuba, "Monarch butterfly optimization algorithm for localization in wireless sensor networks", 2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA), 2018. https://doi.org/10.1109/RADIOELEK.2018.8376387.

      [25] A. Hossam, A. Bouzidi and M. Riffi, "Elephants Herding Optimization for Solving the Travelling Salesman Problem", Advances in Intelligent Systems and Computing, pp. 122-130, 2019. https://doi.org/10.1007/978-3-030-12065-8_12.

      [26] M. Riffi and M. Bouzidi, "Discrete cuttlefish optimization algorithm to solve the travelling salesman problem", 2015 Third World Conference on Complex Systems (WCCS), 2015. https://doi.org/10.1109/ICoCS.2015.7483231.

      [27] G. Reinelt, "TSPLIB", Elib.zib.de, 1997. [Online]. Available: http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsplib.html. [Accessed: 18- Feb- 2019].

  • Downloads

  • How to Cite

    M. Almufti, S., R. Asaad, R., & W. Salim, B. (2019). Review on Elephant Herding Optimization Algorithm Performance in Solving Optimization Problems. International Journal of Engineering & Technology, 7(4), 6109-6114. https://doi.org/10.14419/ijet.v7i4.28473

    Received date: 2019-03-17

    Accepted date: 2019-03-26

    Published date: 2019-05-11