Algorithm 1 OPGTOS1
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 merge communication strategy
9:	if t == R then
10:	j = 0
11:	for i = 1:2:g do
12:	j = j + 1
13:	Merge groups
14:	%Update the optimal individual for each group
15:	Group(j).silverback = BestGorilla
16:	Group(j).bestfit = BestFitness
17:	end for
18:	g = g/2
19:	end if
20:	for i = 1:g do
21:	Update Group(i) using GTO
22:	Mutate Group(i).silverback${}^{{}^{\prime }}$ using Equation (15)
23:	Select the best fit individual from Group(i).silverback${}^{{}^{\prime }}$ and Group(i).silverback
24:	%Update the optimal individual for the entire population
25:	if Group(i).BestFitness<BestGorilla then
26:	BestGorilla=Group(i).silverback
27:	BestFitness=Group(i).bestfit
28:	end if
29:	if t > 100 then
30:	Opposition-based learning with sliding window strategy
31:	end if
32:	end for
33:	end while
Output:BestGorilla and BestFitness.