Vibrating Particles System Algorithm: Overview, Modifications and Applications


  • Saman Almufti Computer Science & IT, Nawroz University



Metaheuristics, Metaheuristics Classifications, Vibrating Particles System, VPS


Real-world problems are difficult enough to encourages academics to develop innovative, effective problem-solving methods. Generally, metaheuristics algorithms that are  inspired by nature, biology, and physics have a flexibility and capacity to adapt to different situations, metaheuristics based on evolutionary computation algorithms have been widely employed to solve complicated, constrained/unconstrained, single/multiple objective, and Non-deterministic polynomial hard (NP-Hard) optimization problems.

This paper describes Vibrating Particles System (VPS) as a Physics Based metaheuristic algorithm inspired by the free vibration of an under-damping objects for solving complex and real-world optimization problems. Since the appearance of VPS many modifications for improving the performance of the algorithm and has been applied to various Applications in several fields. At the end of this paper, the improvements are listed.


S. M. Almufti, "Historical survey on metaheuristics algorithms," 2019/11/17, vol. 7, no. 1, pp. 1-12, International Journal of Scientific World.

Y. Celik and H. Kutucu, "Solving the Tension/Compression Spring Design Problem by an Improved Firefly Algorithm," 2018.

A. Kaveh and M. I. Ghazaan, "A new meta-heuristic algorithm: Vibrating particles system," Scientia Iranica, vol. 24, no. 2, pp. 551-566, 2017.

S. M. Almufti, R. B. Marqas, P. S. Othman and A. B. Sallow, "Single-based and Population-based Metaheuristics for Solving NP-hard Problems," Iraqi J Sci, vol. 62, no. 5, pp. 1-11, 2021/5/31.

A. Sheta, H. Faris, M. Braik and S. Mirjalili, "Nature-inspired metaheuristics search algorithms for solving the economic load dispatch problem of power system: a comparison study," in Applied nature-inspired computing: algorithms and case studies, Springer, Singapore, 2020, pp. 199-230.

A. I. B. o. H. B. S. F. N. Optimization, Karaboğa, Derviş, 2005.

R. R. Ihsan, S. M. Almufti, B. M. Ormani, R. R. Asaad and R. B. Marqas, "A Survey on Cat Swarm Optimization Algorithm," Asian Journal of Research in Computer Science, vol. 10, no. 2, pp. 22-32, 2021.

R. V. Rao, Teaching Learning Based Optimization Algorithm, Springer, 2016.

V. M. A. Kaveh, Colliding Bodies Optimization, springer, 2015.

S. M. Almufti, U-Turning Ant Colony Algorithm powered by Great Deluge Algorithm for the solution of TSP Problem, 2015.

A. Y. Zebari, H. K. Omer and S. M. Almufti, "A comparative study of particle swarm optimization and genetic algorithm," Journal of Advanced Computer Science & Technology, vol. 8, no. 2, pp. 40-45, 2019.

Z. Tabrizian, G. G. Amiri and M. H. A. Beigy, "Charged System Search Algorithm Utilized for Structural Damage Detection," Shock and Vibration, 2014.

F. S. Lobato and V. S. Jr., "Fish swarm optimization algorithm applied to engineering system design," Latin American Journal of Solids and Structures, vol. 11, no. 1, 2014.

H. M. GENC, I. EKS˙IN and O. K. EROL, "Big bang-big crunch optimization algorithm with local directional moves," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, p. 1359 – 1375, 2013.

L. Abualigah, "Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering," Studies in Computational Intelligence, 2019.

S. M. Almufti, "lion optimization algorithm: Overview, modifications and applications," International Research Journal of Science, Technology, Education, and Management, 2022.

A. Kaveh and N. Farhoudi, "A new optimization method: Dolphin echolocation," Advances in Engineering Software, vol. 59, p. 53–70, 2013.

S. Deb, S. Fong and Z. Tian, "Elephant Search Algorithm for optimization problems," in Tenth International Conference on Digital Information Management (ICDIM), 2015.

R. B. Marqas, S. M. Almufti, H. B. Ahmed and R. R. Asaad, "Grey wolf optimizer: Overview, modifications and applications," International Research Journal of Science, Technology, Education, and Management, vol. 1, no. 1, pp. 44-56, 2021.

S. A. Uymaz and G. Tezel, "Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization Problems," in International Conference on Computer Science, Engineering and Technology, 2014.

A. Kaveh and T. Bakhshpoor, Metaheuristics: Outlines, MATLAB Codes and Examples, Springer, 2019.

S. M. Almufti, "Historical survey on metaheuristics algorithms," International Journal of Scientific World, vol. 7, no. 1, pp. 1-12, 2019.

L. Ou, G. Zeng, Y.-C. Chang and C.-T. Lin, "Multi-Objective Vibration-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Boundary-Following of Mobile-Robot Simulation Environment," in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020.

A. Kaveh and M. I. Ghazaan, "Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints," Acta Mech, 2017.

P. J. Gnetchejo, "Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System," Journal of Power and Energy Engineering, vol. 7, pp. 1-26, 2019.

A. Parmar, Y. Kumar, P. K. Singh and V. Singh, "Vibrating Particle System Algorithm for Hard Clustering problem," SCIENCE & TECHNOLOGY, vol. 27, no. 2, pp. 815 - 827, 2019.

A. Kaveha and S. Sabeti, "Optimal design of monopile offshore wind turbine structures using CBO, ECBO, and VPS algorithms," Scientia Iranica, vol. 26, no. 3, pp. 1232-1248, 2019.

Y. Celik and H. Kutucu, Solving the Tension/Compression Spring Design Problem by an Improved Firefly Algorithm, 2018.




How to Cite

Almufti, S. (2022). Vibrating Particles System Algorithm: Overview, Modifications and Applications. ICONTECH INTERNATIONAL JOURNAL, 6(3), 1–11.