An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Table 2

Parameters setting.

No.	Algorithm	Parameter setting
1	NNA [68]	w = 1/(2 log(2)), c1 = 0.5 + log(2), c2 = c1, pop_size = 50
2	LAPO [69]	F = 0.5, CR = 0.9, pop_size = 40
3	GbABC [70]	SN = 12, limit = 1.12 (popsize/2) D, C = 1.507, pop_size = 24
4	SFLA [38]	c = 1, le = 5, m = 8, n = 5, pop_size = 40
5	SCA [72]	pmodify = 1, PMutate = 0.01, elitism parameter = 2, pop_size = 30
6	SSA [11]	Rpower = 2, Rnorm = 2, ElitistCheck = 1, pop_size = 30
7	GWO [6]	pop_size = 30
8	CMAES [73]	pop_size = 4 + floor(3 log (D)), D is the dimension
9	WQPSO [48]	W = Wmin + (MAX_FES-FES)/MAX_FES (Wmax-Wmin), Wwin = 0.5, Wmax = 1.0, pop_size = 80
10	TSQPSO [57]	W = Wmin + (MAX_FES-FES)/MAX_FES (Wmax−Wmin), Wwin = 0.5, Wmax = 1.0, pop_size = 50
11	SaDE [74]	F ∼N (0.5, 0.3), CR ∼N (CRm, 0.1), mutation strategies and crossover strategies, learngen = 50; pop_size = 50
12	AAA [10]	e = 0.3, delta = 2, Ap = 0.5, pop_size = 40
13	EFLA	m = 6, n = 5, pop_size = 30