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Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050602.

Whale optimization algorithm based on Gaussian mutation and differential evolution


Xinyu Li1, Yingping Su2

Corresponding Author:
Xinyu Li

1School of The First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China

2School of Business, Southwest University, 402460, Chongqing, China


Based on the defect that whale optimization algorithm is easy to fall into local optimization and slow convergence speed, this paper proposes an improved whale optimization algorithm based on Gauss mutation and differential evolution. The algorithm initializes the population through logistic chaos, improves the diversity and randomness of the population, and enhances the global search ability of the algorithm. Gaussian mutation and differential evolution are introduced to enhance the local search ability and improve the search accuracy. The performance of the improved optimization algorithm is tested by 12 benchmark functions. The results show that compared with the basic whale algorithm, particle swarm optimization algorithm and Multi-Verse Optimizer algorithm, IWOA algorithm can improve the convergence speed of the population and improve the search accuracy and stability of the algorithm to a certain extent.


Chaos initialization, Gaussian mutation, Differential evolution, Benchmark functions

Cite This Paper

Xinyu Li, Yingping Su. Whale optimization algorithm based on Gaussian mutation and differential evolution. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 7-13. https://doi.org/10.25236/AJCIS.2022.050602.


[1] Can C, Cao W, Wu M, et al.  A new bat algorithm based on iterative local search and stochastic inertia weight [J]. Expert Systems with Applications, 2018, 104: 202-212.

[2] Tian D,Shi Z. MPSO: Modified particle swarm optimization and it's applications [J]. Swarm & Evolutionary Computation, 2018, 41: 49-68.

[3] Wang F, Zhang H, Li K, et al. A hybrid particle swarm optimization algorithm using adaptive learning strategy [J]. Information Sciences, 2018, 436: 162-177.

[4] Skinderowicz R. An improved ant colony system for the sequential ordering problem [J]. Computers & Operations Research, 2017, 86: 1-17.

[5] Mareli M, Twala B. An adaptive cuckoo search algorithm for optimisation [J]. Applied Computing & Informatics, 2017, 14(2): 107-115.

[6] George Lindfield, John Penny, Introduction to nature inspired optimization [M]. Academic Press, 2017: 69-84.

[7] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer [J]. Advances in Engineering Software. 2014, 69(3): 46-61.

[8] Basturk B, Karaboga D. An artificial bee colony (ABC) algorithm for numeric function optimization. In: Proceedings of the IEEE swarm intelligence symposium; 2006. p. 12–14.

[9] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf Sci 2009; 179: 2232–48.

[10] Johnson DS, Papadimitriou CH, Yannakakis M (1988) How easy is local search? J Comput Syst Sci 37:79–100.

[11] Kaveh A, Khayatazad M. A new meta-heuristic method: ray optimization. Comput Struct 2012; 112: 283–94.

[12] Du H, Wu X, Zhuang J. Small-world optimization algorithm for function optimization. Advances in natural computation. Springer; 2006. p. 264–73.

[13] Shah-Hosseini H. Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 2011;6:132–40. 

[14] Moghaddam FF, Moghaddam RF, Cheriet M. Curved space optimization: A random search based on general relativity theory. 2012. arXiv:1208.2214.

[15] Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2015). Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513. https://doi.org/ 10.1007/s00521-015-1870-7

[16] Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

[17] Storn R. Differential Evolution Research – Trends and Open Questions [J]. Springer Berlin Heidelberg, 2008.

[18] WANG X H, LIU X Y, BAI M H. Population migration algorithm with Gaussian mutat ion and the steepest descent operator [J]. Computer Engineering and Applicaions, 2009, 45(20): 57-60+62(in Chinese).

[19] Rudolph G.Local convergence rates of simple evolutionary algorithms with Cauchy mutations [J]. IEEE transactions on evolutionar y computation, 1997, 1(4): 249-258.