Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to its logical approach, broad applicability, and ability to tackle complex issues encountered in engineering systems. However, GA is known for its high implementation cost and typically requires a large number of iterations. On the other hand, Particle Swarm Optimization (PSO) is a relatively new heuristic technique inspired by the collective behaviors of real organisms. Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family. While they are often seen as competitors, their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand. In this study, we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture. Through extensive experiments and performance evaluations, the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator. The comparative analysis sheds light on the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field.
R. Cazacu, Comparison between the performance of GA and PSO in structural optimization problems, Am. J. Eng. Res., vol. 5, no. 11, pp. 268–272, 2016.
O. Buiga and C. O. Popa, Optimal mass design of a single-stage helical gear unit with genetic algorithms, Proc. Rom. Acad. Ser. A Math. Phys. Tech. Sci. Inf. Sci., vol. 13, no. 3, pp. 243–250, 2012.
R. Cazacu and L. Grama, Steel truss optimization using genetic algorithms and FEA, Procedia Technol., vol. 12, pp. 339–346, 2014.
K. Deb and S. Gulati, Design of truss-structures for minimum weight using genetic algorithms, Finite Elem. Anal. Des., vol. 37, no. 5, pp. 447–465, 2001.
N. Noilublao and S. Bureerat, Simultaneous topology, shape and sizing optimisation of a three-dimensional slender truss tower using multiobjective evolutionary algorithms, Comput. Struct., vol. 89, no. 23&24, pp. 2531–2538, 2011.
K. Sourabh, S. S. Chauhan, and V. Kumar, A review on genetic algorithm: Past, present, and future, Multimed. Tools Appl., vol. 80, pp. 8091–8126, 2021.
P. C. Fourie and A. A. Groenwold, The particle swarm optimization algorithm in size and shape optimization, Struct. Multidiscip. Optim., vol. 23, no. 4, pp. 259–267, 2002.
R. E. Perez and K. Behdinan, Particle swarm approach for structural design optimization, Comput. Struct., vol. 85, no. 19-20, pp. 1579–1588, 2007.
S. Saremi, S. M. Mirjalili, and S. Mirjalili, Unit cell topology optimization of line defect photonic crystal waveguide, Procedia Technol., vol. 12, pp. 174–179, 2014.
R. Cazacu and L. Grama, Structural optimization with genetic algorithms and particle swarm optimization, Ann. ORADEA UNIVERSITY Fascicle Manag. Technol. Eng., vol. 12, no. 22, pp. 19–22, 2013.
M. R. Maheri, M. Askarian, and S. Shojaee, Size and topology optimization of trusses using hybrid genetic-particle swarm algorithms, Iran. J. Sci. Technol. Trans. Civ. Eng., vol. 40, no. 3, pp. 179–193, 2016.
J. H. Holland, Genetic algorithms and the optimal allocation of trials, SIAM J. Comput., vol. 2, no. 2, pp. 88–105, 1973.
J. Kennedy, Review of engelbrecht’s fundamentals of computational swarm intelligence, Genet. Program. Evolvable Mach., vol. 8, no. 1, pp. 107–109, 2007.
O. Z. Maimon and D. Braha, A genetic algorithm approach to scheduling PCBs on a single machine, Int. J. Prod. Res., vol. 36, no. 3, pp. 761–784, 1998.
D. D. Ramírez-Ochoa, L. A. Pérez-Domínguez, E. A. Martínez-Gómez, and D. Luviano-Cruz, PSO, a swarm intelligence-based evolutionary algorithm as a decision-making strategy: A review, Symmetry, vol. 14, no. 3, p. 455, 2022.
K. Habib, X. Lai, A. Wadood, S. Khan, Y. Wang, and S. Xu, Hybridization of PSO for the optimal coordination of directional overcurrent protection relays, Electronics, vol. 11, no. 2, p. 180, 2022.
S. Javed and K. Ishaque, A comprehensive analyses with new findings of different PSO variants for MPPT problem under partial shading, Ain Shams Eng. J., vol. 13, no. 5, p. 101680, 2022.
B. Younes, F. Mohammed, M. Saïd, and M. El Bekkali, 5G uplink interference simulations, analysis and solutions: The case of pico cells dense deployment, Int. J. Electr. Comput. Eng. IJECE, vol. 11, no. 3, p. 2245, 2021.
S. E. Chafi, Y. Balboul, M. Fattah, S. Mazer, M. El Bekkali, and B. Bernoussi, Resource placement strategy optimization for IoT oriented monitoring application, TELKOMNIKA Telecommun. Comput. Electron. Contr., vol. 20, no. 4, p. 788, 2022.
R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Softw. Pract. Exp., vol. 41, no. 1, pp. 23–50, 2011.
M. El Bekkali, B. Bernoussi, M. Fattah, S. Mazer, Y. Balboul, and S. E. Chafi, Cloud computing services, models and simulation tools, Int. J. Cloud Comput., vol. 10, nos. 5&6, p. 533, 2021.
This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/