A COMPARATIVE STUDY ON SWARM INTELLIGENCE ALGORITHMS

Authors

  • Akanksha Bansal Chopra Department of Computer Science, SPM College, University of Delhi, New Delhi, India
  • Monika Arora Department of Analytics/IT/Operations, Amity Business School, Amity University Haryana
  • Lavkush Gupta Department of Computer Science, SPM College, University of Delhi, New Delhi, India

Keywords:

Swarm Intelligence algorithms, meta heuristic, nature inspired

Abstract

Swarm Intelligence algorithms are meta-heuristic and population-based stochastic optimization algorithms. These algorithms are influenced by an intelligent and  collective behaviour of insects or animals such as ants, fireflies, dragonflies, wolves, cuckoo, hawks etc. The behaviour of these insects and animals offers information and strategy to win the hunts in their own way. Their behaviour of hunting uses an optimised approach i.e. they win over their prey by using least number of hunting steps. The algorithms are developed on the basis of their behaviour to solve the real-world problems. This research paper presents a comparative study of various swarm intelligence optimization algorithms.

References

I. Yashoda M. B., Vrinda Shivashetty (2022). Implementation of Enhanced Spider Monkey Optimization for D2D Communication through IoT. ISSN 1816-6075 (Print), 1818-0523 (Online) Journal of System and Management Sciences Vol. 12 (2022) No. 2, pp. 512-530 DOI: 10.33168/JSMS.2022.0228. Available at : Vol.12No.02.28.pdf (aasmr.org)

II. Kumar, V., & Kumar, D. (2021). A systematic review on firefly algorithm: past, present, and future. Archives of Computational Methods in Engineering, 28, 3269-3291.

III. Yang, X. S., & He, X. (2013). Firefly algorithm: recent advances and applications. International journal of swarm intelligence, 1(1), 36-50.

IV. Dorigo, M., Birattari, M., & Stutzle, T. (2006). “Ant colony optimization-Artificial ants as a computational intelligence Technique”, IEEE Computational intelligence magazine.

V. Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805-820.

VI. Abualigah, L., & Diabat, A. (2020). A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Computing and Applications, 32(19), 15533-15556.

VII. Sasikumar Gurumurthy , Hemalatha K. L., D. Pamela , Upendra Roy , Vishwanath P. Hybrid Pigeon Inspired Optimizer-Gray Wolf Optimization for Network Intrusion Detection. Journal of System and Management Sciences Vol. 12 (2022) No. 4, pp. 383-397 DOI:10.33168/JSMS.2022.0423

VIII. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.

IX. Alshinwan, M., Abualigah, L., Shehab, M., Elaziz, M. A., Khasawneh, A. M., Alabool, H., & Hamad, H. A. (2021). Dragonfly algorithm: a comprehensive survey of its results, variants, and applications. Multimedia Tools and Applications, 80(10), 14979-15016.

X. Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft computing, 22(2), 387-408.

XI. Abed-alguni, B. H. (2018). Action-selection method for reinforcement learning based on cuckoo search algorithm. Arabian Journal for Science and Engineering, 43(12), 6771-6785.

XII. Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2020). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53(3), 2237-2264.

XIII. Hatta, N. M., Zain, A. M., Sallehuddin, R., Shayfull, Z., & Yusoff, Y. (2019). Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artificial Intelligence Review, 52(4), 2651-2683.

XIV. Joshi, A. S., Kulkarni, O., Kakandikar, G. M., & Nandedkar, V. M. (2017). Cuckoo search optimization-a review. Materials Today: Proceedings, 4(8), 7262-7269.

XV. Layeb, A. (2011). A novel quantum inspired cuckoo search for knapsack problems. International Journal of bio-inspired Computation, 3(5), 297-305.

XVI. Mohamad, A. B., Zain, A. M., & Nazira Bazin, N. E. (2014). Cuckoo search algorithm for optimization problems—a literature review and its applications. Applied Artificial Intelligence, 28(5), 419-448.

XVII. Yang, X. S. (Ed.). (2013). Cuckoo search and firefly algorithm: theory and applications (Vol. 516). Springer.

XVIII. Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.

XIX. Divyashree, H. B., Puttamadappa, C., & KS, N. P. (2022, October). Hybrid Optimization algorithm for clustering and Routing in WSN. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-7). IEEE.

Additional Files

Published

15-05-2023

How to Cite

Akanksha Bansal Chopra, Monika Arora, & Lavkush Gupta. (2023). A COMPARATIVE STUDY ON SWARM INTELLIGENCE ALGORITHMS. International Education and Research Journal (IERJ), 9(5). Retrieved from http://ierj.in/journal/index.php/ierj/article/view/2753