pso.nh {gena}

Neighbourhood

Description

Constructs a neighbourhood of each particle using particular topology.

Usage

pso.nh(pop.n = 40, method = "ring", par = 3, iter = 1)

Arguments

pop.n

integer representing the size of the population.

method

string representing the topology to be used for construction of the neighbourhood. See 'Details' for additional information.

par

additional parameters to be passed depending on the method.

iter

iteration number of the genetic algorithm.

Details

If method = "ring" then each particle will have par[1] neighbours. By default par[1] = 3. See section 3.2.1 of M. Clerc (2012) for additional details. If method = "wheel" then there is a single (randomly selected) particle which informs (and informed by) other particles while there is no direct communication between other particles. If method = "random" then each particle randomly informs other par[1] particles and itself. Note that duplicates are possible so sometimes each particle may inform less then par[1] particles. By default par[1] = 3. See section 3.2.2 of M. Clerc (2012) for more details. If method = "star" then all particles are fully informed by each other. If method = "random2" then each particle will be self-informed and informed by the j-th particle with probability par[1] (value between 0 and 1). By default par[1] = 0.1.

Value

This function returns a list which i-th element is a vector of particles' indexes which inform i-th particle i.e. neighbourhood of the i-th particle.

References

Maurice Clerc (2012). Standard Particle Swarm Optimisation. HAL archieve.

Examples

# Prepare random number generator
set.seed(123)

# Ring topology with 5 neighbours
pso.nh(pop.n = 10, method = "ring", par = 5)

# Wheel topology
pso.nh(pop.n = 10, method = "wheel")

# Star topology
pso.nh(pop.n = 10, method = "star")

# Random topology where each particle 
# randomly informs 3 other particles
pso.nh(pop.n = 10, method = "random", par = 3)

# Random2 topology wehere each particle could
# be informed by the other with probability 0.2
pso.nh(pop.n = 10, method = "random2", par = 0.2)