Advertisement

Artificial Fish Swarm-Inspired Whale Optimization Algorithm for Solving Multimodal Benchmark Functions

  • Imran Rahman
  • Junita Mohamad-SalehEmail author
  • Noorazliza Sulaiman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)

Abstract

Multimodal benchmark function optimization has gained a growing interest exclusively in the evolutionary computation research field which involves achieving all or most of the multiple solutions contrasting a single best solution. A large number of real-world optimization problems can be considered as multimodal function optimization. Recently introduced Whale Optimization Algorithm (WOA) algorithm is inspired by the hunting behavior of humpback whales. The performance of WOA is very promising but the robustness and convergence need further improvement. In this paper, ‘step equation’ of Artificial Fish Swarm Algorithm (AFSA) was incorporated to enhance the robustness and convergence of the original WOA considering five multimodal test functions (F1–F5) for global numerical optimization. The proposed variant of WOA showed improved performances compared to original WOA in terms of average best fitness, robustness and convergence.

Keywords

Artificial fish swarm algorithm Swarm intelligence Benchmark function Whale optimization algorithm Optimization 

Notes

Acknowledgements

This research is supported by USM Global Fellowship (USM.IPS/USMGF/2/2016) and the Ministry of Higher Education (MOHE) Malaysia Fundamental Research Grant Scheme (Grant no. FRGS/1/2017/203.PELECT.6071371).

References

  1. 1.
    Zhang, Z., Wang, K., Zhu, L., Wang, Y.: A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Syst. Appl. 86, 165–176 (2017)CrossRefGoogle Scholar
  2. 2.
    Yang, X., Zhang, W., Song, Q.: A novel WSNs localization algorithm based on artificial fish swarm algorithm. Int. J. Online Eng. 12, 64–68, (2016)Google Scholar
  3. 3.
    Rahman, I., Mohamad-Saleh, J.: Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: a comprehensive survey. Appl. Soft Comput. 69, 72–130 (2018)CrossRefGoogle Scholar
  4. 4.
    Rahman, I., Mohamad-Saleh, J.: Plug-in electric vehicle charging optimization using bio-inspired computational intelligence methods. Sustainable Interdependent Networks, pp. 135–147. Springer, Berlin (2018)Google Scholar
  5. 5.
    Li, X.: A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China (2003)Google Scholar
  6. 6.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  7. 7.
    Rosely, N.F.L.M., Zain, A.M., Omar, A.H.: Improving simplification performance using FSA: experimental result. Indian J. Sci. Technol. 9, (2016)Google Scholar
  8. 8.
    Kaveh, A., Ghazaan, M.I.: Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mech. Based Des. Struct. Mach. 45, 345–362 (2017)CrossRefGoogle Scholar
  9. 9.
    Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42, 965–997 (2014)CrossRefGoogle Scholar
  10. 10.
    Rahman, I., Vasant, P., Singh, B.S.M., Abdullah-Al-Wadud, M.: Swarm intelligence-based optimization for PHEV charging stations. Handbook of Research on Swarm Intelligence in Engineering, p. 374 (2015)Google Scholar
  11. 11.
    Lim, W.H., Isa, N.A.M.: Particle swarm optimization with dual-level task allocation. Eng. Appl. Artif. Intell. 38, 88–110 (2015)CrossRefGoogle Scholar
  12. 12.
    Oliva, D., El Aziz, M.A., Hassanien, A.E.: Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl. Energy 200, 141–154 (2017)CrossRefGoogle Scholar
  13. 13.
    Touma, H.J.: Study of the economic dispatch problem on IEEE 30-bus system using whale optimization algorithm. Int. J. Eng. Technol. Sci. 5, 1 (2016)Google Scholar
  14. 14.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report 2005005 (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Imran Rahman
    • 1
  • Junita Mohamad-Saleh
    • 1
    Email author
  • Noorazliza Sulaiman
    • 2
  1. 1.School of Electrical & Electronic EngineeringUniversiti Sains Malaysia (USM), Engineering CampusNibong TebalMalaysia
  2. 2.Faculty of Engineering TechnologyUniversiti Malaysia PahangGambang, KuantanMalaysia

Personalised recommendations