An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Algorithm 1

Pseudo code of SFLA.

(1)	The pseudo code of SFLA ( )
(2)	FES = 0;//fitness evaluation number
(3)	Randomized initialization and evaluate fitness values f(xk), for k = 1, 2, …, ps.
(4)	FES = FES + ps;
(5)	While FES< = MAX_FES//the max fitness evaluation number
(6)	Sort and arrange population (ps = ) according to the fitness, where m is the number of memeplexes, n is the number of frogs in each memeplex, and k is the local iteration number.
(7)	Get the global best frog ;
(8)	For i = 1 to m do//m memeplexes
(9)	For j = 1 to k do//k is the local iteration number in i^th memeplex
(10)	Get the worst and best frog xworst, xbest in the ith memeplex; //n frogs in ith memeplex
(11)	=  + rand (X best − X worst);
(12)	FES = FES + 1;
(13)	If < f
(14)	=
(15)	ELSEIF  >=
(16)	=  + rand ( −−);
(17)	If < f
(18)	=
(19)	End IF
(20)	FES = FES + 1;
(21)	ELSEIF  > =
(22)	=  + rand (1, D) (−); //lb, ub denote the search boundary
(23)	END IF
(24)	End for//m frogs
(25)	End for//n memeplexes
(26)	End while