Abstract
In this paper, two novel meta-heuristic algorithms are introduced to solve global optimization problems inspired by the Grey Wolf Optimizer (GWO) algorithm. In the GWO algorithm, wolves are likely to be located in regions close to each other. Therefore, as they catch the hunt (approaching the solution), they may create an intensity in the same or certain regions. In this case, the mechanism to prevent the escape of the hunt may not work well. First, the proposed algorithm is the expanded model of the GWO algorithm that is called expanded Grey Wolf Optimizer. In this method, the same as GWO, alpha, beta, and delta play the role of the main three wolves. However, the next wolves select and update their positions according to the previous and the first three wolves in each iteration. Another proposed algorithm is based on the incremental model and is, therefore, called incremental Grey Wolf Optimizer. In this method, each wolf updates its own position based on all the wolves selected before it. There is the possibility of finding solutions (hunts) quicker than according to other algorithms in the same category. However, they may not always guarantee to find a good solution because of their act dependent on each other. Both algorithms focus on exploration and exploitation. In this paper, the proposed algorithms are simulated over 33 benchmark functions and the related results are compared with well-known optimization algorithms. The results of the proposed algorithms seem to be good solutions for various problems.














Similar content being viewed by others
References
Winston PH (1992) Artificial intelligence, 3rd edn. Addison-Wesley, Boston
Yao X, Yong L (1997) Fast evolution strategies. In: International conference on evolutionary programming. Springer, Berlin
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, Hoboken
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Jamil M, Xin-She Y (2013) A literature survey of benchmark functions for global optimization problems. arXiv preprint arXiv.1308-4008
Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73
Chawla P, Chana I, Rana A (2015) A novel strategy for automatic test data generation using soft computing technique. Front Comput Sci 9(3):346–363
Gomes GF, de Almeida FA, Junqueira DM, da Cunha Jr SS, Ancelotti AC Jr (2019) Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods. Eng Struct 181:111–123
Kilinc M, Caicedo JM (2019) Finding plausible optimal solutions in engineering problems using an adaptive genetic algorithm. Adv Civ Eng 2019:1–9
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Sharma R, Vashisht V, Singh AV, Kumar S (2019) Analysis of existing clustering algorithms for wireless sensor networks. System Performance and Management Analytics. Springer, Singapore, pp 259–277
Mann PS, Singh S (2019) Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks. Soft Comput 23(3):1021–1037
Sahu RK, Sekhar GC, Priyadarshani S (2019) Differential evolution algorithm tuned tilt integral derivative controller with filter controller for automatic generation control. Evol Intell. https://doi.org/10.1007/s12065-019-00215-8
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102
Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley-IEEE Press, Hoboken
Zhang X, Luo J, Sun X, Xie J (2019) Optimal reservoir flood operation using a decomposition-based multi-objective evolutionary algorithm. Eng Optim 51(1):42–62
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Kasilingam F, Pasupuleti J, Bharatiraja C, Adedayo Y (2019) Power system stabilizer optimization using BBO algorithm for a better damping of rotor oscillations owing to small disturbances. FME Trans 47(1):166–176
Kumar M, Om H (2019) A Hybrid bio-inspired algorithm for protein domain problems. In: Advances in nature-inspired computing and applications. Springer, Cham, pp 291–311
Bhattacharya A, Chattopadhyay PK (2010) Solving complex economic load dispatch problems using biogeography-based optimization. Expert Syst Appl 37(5):3605–3615
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Naserbegi A, Aghaie M, Minuchehr A, Alahyarizadeh G (2018) A novel exergy optimization of Bushehr nuclear power plant by gravitational search algorithm (GSA). Energy 148:373–385
Marzband M, Ghadimi M, Sumper A, Domínguez-García JL (2014) Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Appl Energy 128:164–174
Chakraborti T, Sharma KD, Chatterjee A (2014) A novel local extrema based gravitational search algorithm and its application in face recognition using one training image per class. Eng Appl Artif Intell 34:13–22
Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111
Sakthivel S, Pandiyan SA, Marikani S, Selvi SK (2013) Application of big bang big crunch algorithm for optimal power flow problems. Int J Eng Sci 2(4):41–47
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289
Özyön S, Temurtaş H, Durmuş B, Kuvat G (2012) Charged system search algorithm for emission constrained economic power dispatch problem. Energy 46(1):420–430
Lam AY, Li VO (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399
Xu J, Lam AY, Li VO (2011) Chemical reaction optimization for task scheduling in grid computing. IEEE Trans Parallel Distrib Syst 22(10):1624–1631
Li Z, Li Y, Yuan T, Chen S, Jiang S (2019) Chemical reaction optimization for virtual machine placement in cloud computing. Appl Intell 49(1):220–232
Kabir R, Islam R (2019) Chemical reaction optimization for RNA structure prediction. Appl Intell 49(2):352–375
Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–491
Haghighi A, Ramos HM (2012) Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization. Water Resour Manag 26(8):2347–2363
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184
Hatamlou A (2018) Solving travelling salesman problem using black hole algorithm. Soft Comput 22(24):8167–8175
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95 - international conference on neural networks, Australia, pp 1942–1948
Pattanayak S, Agarwal S, Choudhury BB, Sahoo SC (2019) Path planning of mobile robot using PSO algorithm. In: Information and communication technology for intelligent systems. Springer, Singapore, pp 515–522
Syahputra R, Robandi I, Ashari M (2015) Reconfiguration of distribution network with distributed energy resources integration using PSO algorithm. Telkomnika 13(3):759
Dorigo M, Birattari M (2010) Ant colony optimization. Springer, New York, pp 36–39
Okdem S, Karaboga D (2009) Routing in wireless sensor networks using an ant colony optimization (ACO) router chip. Sensors 9(2):909–921
Yi W, Kumar A (2007) Ant colony optimization for disaster relief operations. Transp Res Part E Logist Transp Rev 43(6):660–672
Tian J, Yu W, Xie S (2008) An ant colony optimization algorithm for image edge detection. In: 2008 IEEE congress on evolutionary computation (IEEE World Congress on Computational Intelligence). IEEE, pp 751–756
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Gong D, Han Y, Sun J (2018) A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowl Based Syst 148:115–130
Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9(2):625–631
Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, New York, pp 65–74
Osaba E, Yang XS, Diaz F, Lopez-Garcia P, Carballedo R (2016) An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng Appl Artif Intell 48:59–71
Sathya MR, Ansari MMT (2015) Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system. Int J Electr Power Energy Syst 64:365–374
Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Comput 2:78–84
Banati H, Bajaj M (2011) Fire fly based feature selection approach. Int J Comput Sci Issues (IJCSI) 8(4):473
Talatahari S, Gandomi AH, Yun GJ (2014) Optimum design of tower structures using firefly algorithm. Struct Des Tall Spec Build 23(5):350–361
Tuba E, Tuba M, Beko M (2017) Mobile wireless sensor networks coverage maximization by firefly algorithm. In: 2017 27th international conference Radioelektronika (RADIOELEKTRONIKA). IEEE, pp 1–5
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World congress on nature and biologically inspired computing (NaBIC), pp 210–214
Mohamad A, Zain AM, Bazin NEN, Udin A (2013) Cuckoo search algorithm for optimization problems-a literature review. Applied mechanics and materials, vol 421. Trans Tech Publications, Zurich, pp 502–506
Rath A, Samantaray S, Swain PC (2019) Optimization of the cropping pattern using cuckoo search technique. Smart techniques for a smarter planet. Springer, Cham, pp 19–35
Arif MA, Mohamad MS, Latif MSA, Deris S, Remli MA, Daud KM, Corchado JM (2018) A hybrid of cuckoo search and minimization of metabolic adjustment to optimize metabolites production in genome-scale models. Comput Biol Med 102:112–119
Dhivya M, Sundarambal M (2011) Cuckoo search for data gathering in wireless sensor networks. Int J Mob Commun 9(6):642–656
Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. AIP Conf Proc 953(1):162–173
Zhou Y, Chen X, Zhou G (2016) An improved monkey algorithm for a 0–1 knapsack problem. Appl Soft Comput 38:817–830
Yi TH, Li HN, Zhang XD (2015) Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm. Struct Control Health Monit 22(1):123–138
Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76
Li Q, Chen H, Huang H, Zhao X, Cai Z, Tong C, Tian X (2017) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med 2017:1–15
Fahad M, Aadil F, Khan S, Shah PA, Muhammad K, Lloret J, Mehmood I (2018) Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Comput Electr Eng 70:853–870
Mousavi S, Mosavi A, Varkonyi-Koczy AR (2017) A load balancing algorithm for resource allocation in cloud computing. In: International conference on global research and education. Springer, Cham, pp 289–296
Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 8:1–16
Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413–435
Joshi H, Arora S (2017) Enhanced grey wolf optimization algorithm for global optimization. Fundam Inform 153(3):235–264
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, vol 635
Liang JJ, Qu BY, Suganthan PN, Chen Q (2014) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, vol 29, pp 625–640
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Seyyedabbasi, A., Kiani, F. I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems. Engineering with Computers 37, 509–532 (2021). https://doi.org/10.1007/s00366-019-00837-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00366-019-00837-7