Algorithm 2: OPGTOS2
Input: The maximum number of iterations T, the population size N, the lower boundary lb, the upper boundary ub, the dimension dim, the objective function fobj;
1:	Initialization: Generate the individual $X_i(i=1,2,\cdots ,N)$.
2:	%Opposition-based learning
3:	Calculate the opposition-based individual $X_i^{{}^{\prime }}(i=1,2,\cdots ,N)$ using Equation (14). Select N individuals with the best fitness from X${}_i$ and X${}_i^{{}^{\prime }}$;
4:	Divide the population into g groups, every group is $Group\left(j\right)(j=1,2,\cdots ,g)$;
5:	The best gorilla in each group is Group(j).silverback and its fitness value is Group(j).bestfit;
6:	t = 1;
7:	while t < T do
8:	%Multi-group competition communication strategy
9:	for i = 1:g do
10:	if t == R${}_1$ then
11:	Randomly select some individuals in Group(i)
12:	Mutate the selected individuals using Equation (18)
13:	Update Group(i).silverback using Equation (19)
14:	end if
15:	if t == R${}_2$ then
16:	Update BestGorilla using Equation (20)
17:	Update Group(i).silverback using Equation (21)
18:	end if
19:	end for
20:	for i = 1:g do
21:	Update Group(i) using GTO
22:	%Update the optimal individual for the entire population
23:	if Group(i).BestFitness<BestGorilla then
24:	BestGorilla=Group(i).silverback
25:	BestFitness=Group(i).bestfit
26:	end if
27:	if t > 100 then
28:	Opposition-based learning with sliding window strategy
29:	end if
30:	end for
31:	end while
Output:BestGorilla and BestFitness.