cbx.dynamics.PSO#
- class cbx.dynamics.PSO(f, m=0.001, gamma=None, lamda_memory=0.4, sigma_memory=None, **kwargs)[source]#
Bases:
CBXDynamicParticle Swarm Optimization class
This class implements the PSO algorithm as described in [1] and [2]. The algorithm is a particle dynamic algorithm that is used to minimize the objective function \(f(x)\).
- Parameters:
f (objective) – The objective function \(f(x)\) of the system.
x (array_like, shape (N, d)) – The initial positions of the particles. For a system of \(N\) particles, the i-th row of this array
x[i,:]represents the position \(x_i\) of the i-th particle.y (array_like, shape (N, d)) – The initial positions of the particles. For a system of \(N\) particles, the i-th row of this array
y[i,:]represents the or an approximation of the historical best position \(y_i\) of the i-th particle.v (array_like, shape (N, d)) – The initial velocities of the particles. For a system of \(N\) particles, the i-th row of this array
y[i,:]represents the or an approximation of the historical best position \(y_i\) of the i-th particle.dt (float, optional) – The parameter \(dt\) of the system. The default is 0.1.
alpha (float, optional) – The heat parameter \(\alpha\) of the system. The default is 1.0.
m (float, optional) – The inertia \(m\) of the system. The default is 0.1.
gamma (float, optional) – The friction coefficient \(\gamma\) of the system. The default is 1-m.
lamda (float, optional) – The decay parameter \(\lambda\) of the system. The default is 1.0.
noise (noise_model, optional) – The noise model that is used to compute the noise vector. The default is
normal_noise(dt=0.1).sigma (float, optional) – The parameter \(\sigma\) of the noise model. The default is 1.0.
lamda_memory (float, optional) – The decay parameter \(\lambda_{\text{memory}}\) of the system. The default is 1.0.
sigma_memory (float, optional) – The parameter \(\sigma_{\text{memory}}\) of the noise model. The default is 1.0.
References