Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
Algorithm 1
Particle swarm optimization algorithm.
(1)
Define: let f() be the fitness function, N is the number of particles, D is the number of dimensions, xi and are the position and velocity of each particle, respectively, is the best known position of particle i, and is the best known position of the entire swarm.
(2)
Output: // the optimal solution
(3)
// initialize the swarm
(4)
for i ⟵ 1 to N do // each particle
(5)
for d ⟵ 1 to D do // each dimension
(6)
// lb and ub are the lower and upper boundaries of the search space
(7)
(8)
end
(9)
⟵ xi
(10)
if then
(11)
⟵
(12)
end
(13)
end
(14)
while iter < max_iter do // iterate until termination criterion met
