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, x_{i} 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)

⟵ x_{i}

(10)

if then

(11)

⟵

(12)

end

(13)

end

(14)

while iter < max_iter do // iterate until termination criterion met

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.