A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting

Algorithm 1

PIO algorithm.

	Input
NP: number of individuals in pigeon swarm
D: dimension of the search space
R: the map and compass factor
Search range: the borders of the search space
Nc1max: the maximum number of generations that the map and compass operation is carried out
Nc2max: the maximum number of generations that the landmark operation is carried out.
Output
Xg: the global optima of the fitness function f
(1)	Initialization
Set initial values for Nc1max, Nc2max, NP, D, R and the search range
Set initial path Xi and velocity Vi for each pigeon individual
Set Xp = Xi, NC = 1
Calculate fitness values of different pigeon individuals
: = argmin [f: (Xp)]
(2)	Map and compass operations
For Nc = 1 to Nc1max do
for i = 1 to Np do
while Xi is beyond the search range do
calculate Vi and Xi according to equations (4) and (5)
end while
end for
evaluate Xi, and update Xp and Xg
end for
(3)	Landmark operations
For Nc = Nc1max + 1 to Nc2max do
while Xp is beyond the search range do
rank all the available pigeon individuals according to their fitness values
NP = NP/2
keep half of the individuals with better fitness value, and abandon the other half
Xc = average value of the paths of the remaining pigeon individuals
calculate Xi according to equation (8)
end while
evaluate Xi, and update Xp and
end for
(4)	Output
is output as the global optima of the fitness function f