Abstract: In the standard grey wolf optimizer (GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer (DGWO1) and the second dynamic grey wolf optimizer (DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.

改进的动态灰狼优化算法

[1]Al-Betar MA, Awadallah MA, Faris H, et al., 2018. Natural selection methods for grey wolf optimizer. Expert Syst Appl, 113:481-499.

[2]Cong SL, Sun J, Mao HP, et al., 2018. Non-destructive detection for mold colonies in rice based on hyperspectra and GWO-SVR. J Sci Food Agric, 98(4):1453-1459.

[3]Daniel E, 2018. Optimum wavelet based homomorphic medical image fusion using hybrid genetic-grey wolf optimization algorithm. IEEE Sens J, 18(6):6804-6811.

[4]Emary E, Zawbaa HM, Hassanien AE, 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172:371-381.

[5]Gupta S, Deep K, 2018. Cauchy grey wolf optimiser for continuous optimisation problems. J Exp Theor Artif Intell, 30(6):1051-1075.

[6]Gupta S, Deep K, 2019a. Hybrid grey wolf optimizer with mutation operator. In: Bansal JC, Das KN, Nagar A, et al. (Eds.), Soft Computing for Problem Solving. Springer, Singapore, p.961-968.

[7]Gupta S, Deep K, 2019b. A novel random walk grey wolf optimizer. Swarm Evol Comput, 44:101-112.

[8]Gupta S, Deep K, 2019c. An opposition-based chaotic grey wolf optimizer for global optimisation tasks. J Exp Theor Artif Intelll, 31(5):751-779.

[9]Gupta S, Deep K, 2020. A memory-based grey wolf optimizer for global optimization tasks. Appl Soft Comput, 93: 106367.

[10]Gupta S, Deep K, Moayedi H, et al., 2020. Sine cosine grey wolf optimizer to solve engineering design problems. Eng Comput, online.

[11]Liu XL, Tian Y, Lei XH, et al., 2019. An improved self-adaptive grey wolf optimizer for the daily optimal operation of cascade pumping stations. Appl Soft Comput, 75:473-493.

[12]Long W, Jiao JJ, Liang XM, et al., 2018. An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng Appl Artif Intell, 68:63-80.

[13]Lu C, Xiao SQ, Li XY, et al., 2016. An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Adv Eng Softw, 99:161-176.

[14]Mirjalili S, Mirjalili SM, Lewis A, 2014. Grey wolf optimizer. Adv Eng Softw, 69:46-61.

[15]Mirjalili S, Saremi S, Mirjalili SM, et al., 2016. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl, 47:106-119.

[16]Qais MH, Hasanien HM, Alghuwainem S, 2018. Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems. Appl Soft Comput, 69: 504-515.

[17]Rodríguez L, Castillo O, Soria J, et al., 2017. A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl Soft Comput, 57:315-328.

[18]Sahoo BP, Panda S, 2018. Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control. Sustain Energy Grids Netw, 16:278-299.

[19]Saremi S, Mirjalili SZ, Mirjalili SM, 2015. Evolutionary population dynamics and grey wolf optimizer. Neur Comput Appl, 26(5):1257-1263.

[20]Saxena A, Kumar R, Das S, 2019. β-Chaotic map enabled grey wolf optimizer. Appl Soft Comput, 75:84-105.

[21]Tripathi AK, Sharma K, Bala M, 2018. A novel clustering method using enhanced grey wolf optimizer and MapReduce. Big Data Res, 14:93-100.

[22]Wu GH, Mallipeddi R, Suganthan PN, 2016. Problem Definitions and Evaluation Criteria for the CEC 2017 Competition and Special Session on Constrained Single Objective Real-Parameter Optimization. Technical Report, No. 201212, Nanyang Technological University, Singapore.

[23]Zawbaa HM, Emary E, Grosan C, et al., 2018. Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach. Swarm Evol Comput, 42:29-42.

[24]Zhang S, Zhou YQ, 2015. Grey wolf optimizer based on Powell local optimization method for clustering analysis. Discr Dynam Nat Soc, 2015:481360.

[25]Zhang XM, Kang Q, Cheng JF, et al., 2018. A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer. Appl Soft Comput, 67:197-214.

[26]Zhang XQ, Ming ZF, 2017. An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application. Front Inform Technol Electron Eng, 18(11):1705-1719.