Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey: Whale Optimization Algorithm for Route Optimization Problems

Himani Yadav, Dr. Umesh Lilhore, Nitin Agrawal


This work presents wireless optimization with the help of a Meta heuristic algorithm from the group of swarm algorithms named as Whale Optimization Algorithm (WOA). It is a multi-objective algorithm that can be used in solving more than one problem. This algorithm copies the hunting behaviour of the one of the whale species named as humpback whale. Whale optimization algorithm copies the spiral net technique, in which the whale creates a spiracle net of bubbles for encircling the pray and then find the prey either randomly or whale have to apply best find strategy to find the moving prey. This algorithm can be used in many fields like ad-hoc, wireless network, power electronics, and classical engineering problems. But here in this paper it is going to apply on route finding Problem in wireless network. Where the wireless network require to find the location of moving device and also needed to find the optimal path from one device to another for data delivery. In such type of problems Whale optimization algorithm can produce batter results then tradition algorithm.


Ad hoc Network, Meta Heuristic, Swarm Techniques, Whale Optimization

Full Text:



S. Mirjalili, A. Lewis, The Whale Optimization Algorithm, Advances in,.2016,.DOI: vengsoft.2016.01.008

X. Li, A new intelligent optimization-artificial fish swarm algorithm Doctor Thesis, Zhejiang University of Zhejiang, China, 2003.

M. Roth, W. Stephen, Termite: A swarm intelligent routing algorithm for mobilewireless Ad-Hoc networks, in: Stigmergic Optimization, Springer Berlin Heidelberg, 2006, pp. 155-184.

A. Kaveh , N. Farhoudi, A new optimization method: Dolphin echolocation, Advances in Engineering Software, 59, p.53-70, May, 2013 [doi>10.1016/j.advengsoft.2013.03.004

B. Zeng, Y. Dong, An improved harmony search based energyefficientrouting algorithm for wireless sensor networks, Applied Soft Computing, 41 (2016) 135-147.

Hongping Hu, Yanping Bai, and Ting Xu Improved whale optimization algorithms based on inertia weights and theirs applications , INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING Volume 11, 2017.

Mohammad Rohani, GholamaliShafabakhsh, Abdolhosein Haddad and EhsanAsnaashariThe workflow planning of construction sites using whale optimization algorithm (WOA), The Turkish Online Journal of Design, Art and Communication, 10.7456/1060NVSE/107.

X.N.Li,G.F.Yang,“Artificial bee colony algorithm with memory, ”Applied Soft Computing, vol.41,pp.362-372,2016.

Bing Zeng , Liang Gao and Xinyu Li ,Whale swarm algorithm for function optimization.

K.C. Tan, Y. Chew, L.H. Lee, A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems, European Journal of Operational Research, 172 (2006) 855-885.

Y. Tan, Y. Zhu, Fireworks algorithm for optimization, Springer, 2010.

C. Prodhon A hybrid evolutionary algorithm for the peri¬odic location-routing problem. European Journal of Oper¬ational Research. 2011 Apr; 210(2):204–12.

Cellular Genetic Algorithms. IJCSNS International Jour¬nal of Computer Science and Network Security. 2012 Feb; 12(2).

C Selvaraj, R Siva Kumar, M Karnan. A survey on appli¬cation of bio-inspired algorithms. International Journal of Computer Science and Information Technologies. 2014; 5(1):366–70.

CBlum, ARoli. Metaheuristics in combinatorial optimi¬zation: Overview and conceptual comparison. ACM Com¬puting Surveys. 2003 Sep; 35(3):268–308.

CArchetti, . An opti¬mization-based heuristic for the split delivery vehicle rout¬ing problem. Journal Transportation Science archive. 2008 Feb; 42(1):22–31.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.