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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Review Article

A Review Study of Modified Swarm Intelligence: Particle Swarm Optimization, Firefly, Bat and Gray Wolf Optimizer Algorithms

Author(s): Chinwe P. Igiri*, Yudhveer Singh and Ramesh C. Poonia

Volume 13, Issue 1, 2020

Page: [5 - 12] Pages: 8

DOI: 10.2174/2213275912666190101120202

Price: $65

Abstract

Background: Limitations exist in traditional optimization algorithms. Studies show that bio-inspired alternatives have overcome these drawbacks. Bio-inspired algorithm mimics the characteristics of natural occurrences to solve complex problems. Particle swarm optimization, firefly algorithm, bat algorithms, gray wolf optimizer, among others are examples of bio-inspired algorithms. Researchers make certain assumptions while designing these models which limits their performance in some optimization domains. Efforts to find a solution to deal with these challenges leads to the multiplicity of variants.

Objectives: This study explores the improvement strategies in four popular swarm intelligence in the literature. Specifically, particle swarm optimization, firefly algorithm, bat algorithm, and gray wolf optimizer. It also tries to identify the exact modification position in the algorithm kernel that yielded the positive outcome. The primary goal is to understand the trends and the relationship in their performance.

Methods: The best evidence review methodology approach is employed. Two ancient but valuable and two recent and efficient swarm intelligence, are selected for this study.

Results: Particle swarm optimization, firefly algorithm, bat algorithm, and gray wolf optimizer exhibit local optima entrapment in their standard states. The same enhancement strategy produced effective outcome across these four swarm intelligence. The exact approach is chaotic-based optimization. However, the implementation produced the desired result at different stages of these algorithms.

Conclusion: Every bio-inspired algorithm comprises two or more updating functions. Researchers need a proper guide on what and how to apply a strategy for an optimum result.

Keywords: Computational optimization, metaheuristic, swarm intelligence, particle swarm optimization, firefly algorithm, bat algorithm, grey wolf optimizer.

Graphical Abstract

[1]
X. Yang, Nature-inspired metaheuristic algorithms., Luniver Press: U.K., 2010.
[2]
G. Kaur, and S. Arora, "Chaotic whale optimization algorithm", J. Comput. Design Eng., vol. 5, no. 3, pp. 275-284, 2018.
[3]
S. Kumar, and R. Kumari, "Artificial bee colony, firefly swarm optimization, and bat algorithms", In: Advances in Swarm Intelligence for Optimizing Problems in Computer Science., Chapman and Hall/CRC, pp. 145-182. 2018
[http://dx.doi.org/10.1201/9780429445927-6]
[4]
S. Arora, and S. Singh, "Node localization in wireless sensor networks using butterfly optimization algorithm", Arab. J. Sci. Eng., vol. 42, no. 8, pp. 3325-3335, 2017.
[http://dx.doi.org/10.1007/s13369-017-2471-9]
[5]
J. Kennedy, and R. Eberhart, "Particle swarm optimization”, In Proccedings of the IEEE International Conference on Neural networks,", 1942
[http://dx.doi.org/10.1109/ICNN.1995.488968]
[6]
Y. Shi, "Particle swarm optimization: Developments, applications and resources", In: ' IEEE Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546),. ol. 1, pp. 81-86, 2001.
[7]
Z. He, C. Wei, L. Yang, X. Gao, S. Yao, R. Eberhart, and Y. Shi, "Extracting rules from fuzzy neural network by particle swarm optimisation", IEEE International Conference on Evolutionary Computation Proceedings, 1998
[8]
M. Clerc, "The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization", In: In Proceedings of the Congress on Evolutionary Computation-CEC99, 1999.
[http://dx.doi.org/10.1109/CEC.1999.785513]
[9]
R.C. Eberhart, and Y. Shi, "Evolving artificial neural networks", In: Proceedings of the International Conference on Neural Networks and Brain. Vol. 1, pp. PL5-PLI3, 1998.
[10]
R.C. Eberhart, and Y. Shi, "Comparison between genetic algorithms and particle swarm optimization", In: International Conference on Evolutionary Programming. pp. 611-616, 1998.
[http://dx.doi.org/10.1007/BFb0040812]
[11]
L. Liu, L. Wang, Y. Jin, F. Tang, and D. Huang, "Improved particle swarm optimization combined with chaos", Chaos Solitons Fractals, vol. 25, no. 5, pp. 1261-1271, 2005.
[http://dx.doi.org/10.1016/j.chaos.2004.11.095]
[12]
M.F. El-Santawy, A.N. Ahmed, Z. El-Dean, and A. Ramadan, "Chaotic harmony search optimizer for solving numerical integration", Comput. Inf. Syst., vol. 16, no. 2, 2012.
[13]
H. Afrabandpey, M. Ghaffari, A. Mirzaei, and M. Safayani, "A novel bat algorithm based on chaos for optimization tasks", In: InIEEE Iranian Conference on Intelligent Systems (ICIS), pp. 1-6. 2014
[http://dx.doi.org/10.1109/IranianCIS.2014.6802527]
[14]
G. Kaur, and S. Arora, "Chaotic whale optimization algorithm", J. Comput. Design Eng., vol. 5, no. 3, pp. 275-284, 2018.
[15]
X. Yang, and X. He, "Firefly algorithm: Recent advances and applications", Int. J. Swarm Intelligence, vol. 1, no. 1, p. 36, 2013.
[http://dx.doi.org/10.1504/IJSI.2013.055801]
[16]
M. Sayadi, R. Ramezanian, and N. Ghaffari-Nasab, "A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems", Int. J. Industrial Eng. Comput., vol. 1, no. 1, pp. 1-10, 2010.
[http://dx.doi.org/10.5267/j.ijiec.2010.01.001]
[17]
X. Yang, "Firefly algorithm, lévy flights and global optimization", Res. Develop. Intelligent Syst., vol. XXVI, pp. 209-218, 2009.
[18]
I. Pavlyukevich, "Lévy flights, non-local search and simulated annealing", J. Comput. Phys., vol. 226, no. 2, pp. 1830-1844, 2007.
[http://dx.doi.org/10.1016/j.jcp.2007.06.008]
[19]
A. Gandomi, X. Yang, S. Talatahari, and A. Alavi, "Firefly algorithm with chaos", Commun. Nonlinear Sci. Numer. Simul., vol. 18, no. 1, pp. 89-98, 2013.
[http://dx.doi.org/10.1016/j.cnsns.2012.06.009]
[20]
C.H. Di He, L.G. Jiang, H.W. Zhu, and G.R. Hu, "Chaotic characteristics of a one-dimensional iterative map with infinite collapses", IEEE Trans. Circ. Syst. I Fundam. Theory Appl., vol. 48, no. 7, pp. 900-906, 2001.
[http://dx.doi.org/10.1109/81.933333]
[21]
X. Yang, and X. He, "Bat algorithm: Literature review and applications", Int. J. Bio-inspired Comput., vol. 5, no. 3, p. 141, 2013.
[http://dx.doi.org/10.1504/IJBIC.2013.055093]
[22]
A. Rezaee Jordehi, J. Jasni, N. Abd Wahab, M. Kadir, and M. Javadi, "Enhanced Leader PSO (ELPSO): A new algorithm for allocating distributed TCSC’s in power systems", Int. J. Electr. Power Energy Syst., vol. 64, pp. 771-784, 2015.
[http://dx.doi.org/10.1016/j.ijepes.2014.07.058]
[23]
R. Jordehi, "Chaotic Bat Swarm Optimisation (CBSO)", Appl. Soft Comput., vol. 26, pp. 523-530, 2015.
[http://dx.doi.org/10.1016/j.asoc.2014.10.010]
[24]
O.S. Soliman, and E. Abo El-Hamd, "A chaotic levy flights bat algorithm for diagnosing diabetes mellitus", Int. J. Comput. Appl., vol. 111, no. 1, pp. 36-42, 2015.
[25]
S. Mirjalili, S. Mirjalili, and A. Lewis, "Grey wolf optimizer", Adv. Eng. Softw., vol. 69, pp. 46-61, 2014.
[http://dx.doi.org/10.1016/j.advengsoft.2013.12.007]
[26]
H. Xu, X. Liu, and J. Su, "An improved grey wolf optimizer algorithm integrated with Cuckoo Search", In: 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).Bucharest, 2017, pp. 490-493.
[http://dx.doi.org/10.1109/IDAACS.2017.8095129]
[27]
M. Kohli, and S. Arora, "Chaotic grey wolf optimization algorithm for constrained optimization problems", J. Comput. Design Eng., vol. 5, no. 4, pp. 485-472, 2018.

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