An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

Journal of Electrical Systems and Information Technology

Table 2 Parameter setting of compared and proposed algorithms for UBFs

Algorithm	Year	References	Control parameter		Population size	Stopping criterion	Run
Algorithm	Year	References	Term	Values	Population size	Stopping criterion	Run
EO	2019	[12]	a₁, a₂ and GP	{1, 1.5, 2, 2.5, 3}, {0.1, 0.5, 1, 1.5, 2} and (0.1.0.25, 0.5, 0.75, 0.9}	30	500	30
HHO	2019	[5]	Escaping energy	\(E<0.5, E\ge 0.5\)	30	500	30
JADE	2009	[17]	F_i and CR_i,	randc_i(μ_F, 0.1) and randn_i(μ_CR, 0.1)	50	1000	30
SHADE	2013	[18]	Pbest and Arc rate	0.1 and 2	30	500	30
HEPSO	2014	[22]	P_C and P_B	0.95 and 0.02	50	500	30
RPSOLF	2017	[23]	w, c₁, c₂, c₃ and β, ε	0.55, 1.49, 1.49, 1.5 and 0.99	50	500	30
FAPSO	2018	[34]	–	–	50	5000	30
PSOSCALF	2018	[35]	w_min, w_max, c_1min, c_1max, c_2min, c_2max and β	0.4, 0.9, 0.5, 2.5, 0.5, 2.5 and 1.5	50	500	30
aDE	Proposed		–	–	30	500	30
aPSO			\({w}_{i}, {w}_{f}, {c}_{1i}, {c}_{1f}, {c}_{2i}\) and \({c}_{2f}\)	0.4, 0.9, 0.5, 2.5, 2.5 and 0.5	30	500	30
haDEPSO			–	–	30	500	30