def _update_particles(self):
"""
Update the velocities and positions for all particles.
This does not update the fitness for each particle.
"""
# Random values between zero and one. One random value per particle.
rand_g = tools.rand_uniform(size=self.num_particles)
# Update velocity for all particles using numpy operations.
# For an explanation of this formula, see the research papers referenced above.
# Note that self.best is the swarm's best-known position aka. global-best.
self.velocity = (self.omega * self.velocity.T \
+ self.phi_g * rand_g * (self.best - self.particle).T).T
# Fix de-normalized floating point values which can make the execution very slow.
self.velocity = tools.denormalize_trunc(self.velocity)
# Bound velocity.
self.velocity = tools.bound(self.velocity, self.velocity_lower_bound,
self.velocity_upper_bound)
# Update particle positions in the search-space by adding the velocity.
self.particle = self.particle + self.velocity
# Bound particle position to search-space.
self.particle = tools.bound(self.particle, self.problem.lower_bound,
self.problem.upper_bound)
def _update_particles(self):
"""
Update the velocities and positions for all particles.
This does not update the fitness for each particle.
"""
# Random values between zero and one. One random value per particle.
rand_g = tools.rand_uniform(size=self.num_particles)
# Update velocity for all particles using numpy operations.
# For an explanation of this formula, see the research papers referenced above.
# Note that self.best is the swarm's best-known position aka. global-best.
self.velocity = (self.omega * self.velocity.T \
+ self.phi_g * rand_g * (self.best - self.particle).T).T
# Fix de-normalized floating point values which can make the execution very slow.
self.velocity = tools.denormalize_trunc(self.velocity)
# Bound velocity.
self.velocity = tools.bound(self.velocity, self.velocity_lower_bound, self.velocity_upper_bound)
# Update particle positions in the search-space by adding the velocity.
self.particle = self.particle + self.velocity
# Bound particle position to search-space.
self.particle = tools.bound(self.particle, self.problem.lower_bound, self.problem.upper_bound)