Table 3.

Parameter settings of the single-objective algorithms. For scenario 1, N = 50, Max_t = 500; For scenario 2, N = 100, Max_t = 1000.

Algorithms	Parameters	Values	Algorithms	Parameters	Values
EOSMA	Hybrid parameter z	0.5	IGWO	Convergence factor a	[2, 0]
	Mutation probability q	0 and 1	PSOGSA	Inertia weight w	[1, 0]
	Control volume V	1		Personal cognition coefficient c1	0.5
	Generation probability GP	0.5		Social cognition coefficient c2	1.5
	Exploration factor a1	1 and 2		Gravitational constant G0	1
	Exploitation factor a2	2		Constant α	23
SMA	Constant z	0.03	CODE	Scale factor F	0.5
EO	Control volume V	1		Crossover rate Cr	0.9
	Generation probability GP	0.5		Generation jumping rate Jr	0.3
	Exploration factor a1	2	MTDE	Constant WinIter	20
	Exploitation factor a2	1		Constant H	5
MRFO	Somersault factor S	2		Constant initial	0.001
MPA	Constant p	0.5		Constant final	2
	Constant FADs	0.2		Parameter Mu	log(Dim)
FPA	Scale factor a	2		Constant μf	0.5
	Constant b	0.5		Constant σ	0.2
	Proximity probability p	0.2	SASS	Constant pr	0.11
DE	Scale factor F	0.5		Population size N	[18*Dim, 4]
	Crossover rate Cr	0.9		Rank of diagonal matrix rd	0.5
GBO	Constant pr	0.5		Scale factor c	0.7
TLBO	Teaching factor TF	{1, 2}		Archiving size Ar	1.4
HHO	Constant β	1.5		Memory size Ms	100