Wildebeest optimization algorithm based on swarm intelligence method in solving optimization problems

Document Type : Research Paper


Department of Computer Engineering, Technical and Vocational University (TVU),Tehran, Iran


Metaheuristic algorithms are effective ways to solve optimization problems and use existing phenomena in nature to solve these problems. Due to the independence of metaheuristic algorithms from the gradient information, the objective function can be used to solve large-scale problems by optimization solutions. The organisms’ behavior in nature in their interaction with each other is one of the optimization methods that are modeled as swarm-based algorithms. Swarm-based algorithms are a set of metaheuristic algorithms which are modeled based on group behavior of their organisms and social interactions. The behavior of wildebeests in nature is considered as a swarm-based algorithm for survival because it can be seen that these organisms migrate in groups and try to survive for themselves and their own herd. In this paper, a new metaheuristic algorithm (WOA) based on migratory and displacement behavior of wildebeests is presented of solving optimization problems. In this algorithm, problem solutions are defined as wildebeest herds that search the problem space for appropriate habitat. The results of the implementation of a set of benchmark functions for solving optimization problems such as the Wildebeest Optimization Algorithm, Whale Optimization Algorithm, BAT, Firefly and Particle Swarm Optimization (PSO) algorithms show that the proposed algorithm is less error rate to find global optimum and also caught up rate in the local optimum is less than the methods.


[1] S. Arora and S. Singh, Butterfly optimization algorithm: a novel approach for global optimization, Soft Computing,
(2018) 1-20.
[2] Y. Atay, I. Koc, I. Babaoglu and H. Kodaz, Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms, Applied Soft Computing, 50 (2017) 194-211.
[3] N. Delgarm, B. Sajadi, F. Kowsary and S. Delgarm, Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO), Applied Energy, 170 (2016)
[4] G. Dhiman and V. Kumar, Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for
engineering applications, Advances in Engineering Software, 114 (2017) 48-70.
[5] A. E. S. Ezugwu, A. O. Adewumi and M. E. Frˆıncu, Simulated annealing based symbiotic organisms search
optimization algorithm for traveling salesman problem, Expert Systems with Applications, 77 (2017) 189-210.[6] H. Ismkhan, Effective heuristics for ant colony optimization to handle large-scale problems, Swarm and Evolutionary Computation, 32 (2017) 140-149.
[7] A. Kaveh and T. Bakhshpoori, Water evaporation optimization: a novel physically inspired optimization algorithm, Computers & Structures, 167 (2016) 69-85.
[8] M. D. Li, H. Zhao, H. Weng and T. Han, A novel nature-inspired algorithm for optimization: Virus colony search,
Advances in Engineering Software, 92 (2016) 65-88.
[9] S. Mirjalili, SCA: a sine cosine algorithm for solving optimization problems, Knowledge-Based Systems, 96 (2016)
[10] S. Z. Mirjalili, S. Mirjalili, S. Saremi, H. Faris, and I. Aljarah, Grasshopper optimization algorithm for multiobjective optimization problems, Applied Intelligence, 48(4) (2018) 805-820.
[11] S. Mirjalili and A. Lewis, The whale optimization algorithm, Advances in Engineering Software, 95 (2016) 51-67.
[12] T. T. Nguyen, T. T. Nguyen, A. V. Truong, Q. T. Nguyen and T. A. Phung, Multi-objective electric distribution
network reconfiguration solution using runner-root algorithm, Applied Soft Computing, 52 (2017) 93-108.
[13] S. M. Nigdeli, G. Bekda¸s and X. S. Yang, Application of the flower pollination algorithm in structural engineering,
In Metaheuristics and optimization in civil engineering, (2016) 25-42.
[14] E. Osaba, X. S. Yang, F. Diaz, P. Lopez-Garcia and R. Carballedo, An improved discrete bat algorithm for
symmetric and asymmetric traveling salesman problems, Engineering Applications of Artificial Intelligence, 48
[15] V. K. Patel and V. J. Savsani, A multi-objective improved teaching–learning based optimization algorithm (MOITLBO), Information Sciences, 357 (2016) 182-200.
[16] K. Tang, X. Xiao, J. Wu, J. Yang and L. Luo, An improved multilevel thresholding approach based modified
bacterial foraging optimization, Applied Intelligence, 46(1) (2017) 214-226.
[17] Y. Yuan, H. Xu, B. Wang and X. Yao, A new dominance relation-based evolutionary algorithm for many-objective
optimization, IEEE Transactions on Evolutionary Computation, 20(1) (2016) 16-37.
[18] H. Zaheer, M. Pant, S. Kumar, O. Monakhov, E. Monakhova and K. Deep, A new guiding force strategy for
differential evolution, International Journal of System Assurance Engineering and Management, 8(4) (2017)
[19] Y. J. Zheng, Water wave optimization: a new nature-inspired metaheuristic, Computers & Operations Research,
55 (2015) 1-11.
[20] Y. Zhou, J. K. Hao and B. Duval, Reinforcement learning based local search for grouping problems: A case study
on graph coloring, Expert Systems with Applications, 64 (2016) 412-422.
Volume 12, Special Issue
December 2021
Pages 1397-1410
  • Receive Date: 03 July 2021
  • Revise Date: 19 September 2021
  • Accept Date: 12 October 2021