An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Algorithm 1

Pseudocode of the proposed APSOA.

	: adaptive particle swarm optimization function
Input:
PS: population size
N: subpopulation Size
α: coefficient update rate
D: problem size
θ: fragmentation size
MG: maximum generations of the algorithm
MaxV: an array of D values; MaxVd is the maximum value in the domain of the dth dimension of the problem space
MinV: an array of D values; MinVd is the minimum value in the domain of the dth dimension of the problem space
C1: an array of N values; C1(i) is the first movement coefficient of the ith subpopulation
C2: an array of N values; C2(i) is the second movement coefficient of the ith subpopulation
F: a given objective function
Output:
ĝ: the best found particle
(1)
(2)	For i = 1 : MG
(2.1)	For p = 1 : PS
(2.1.1)
(2.1.2)
(2.1.3)
(2.1.4)
(2.2)
(2.3)
(2.4)
(2.5)
(2.6)
(2.7)
(2.8)
(2.8.1)
(2.8.2)
(2.8.3)	;
(2.8.4)
(2.8.5)
(2.8.6)
(2.8.7)
(2.9)
(2.9.1)