Short and sweet particle swarm optimization implementation in python

• python 3
• numpy

Minimize function:

def simple_error_function(args):
    return args[0]+args[1]

number_of_parameters = 2
max_iterations = 100
best_parameters, best_error_score = particle_swarm_optimize(simple_error_function,
                                        number_of_parameters, max_iterations)

returns the best found parameters and it's score for that function

Custom parameter weight initialization:

def simple_error_function(args):
    return args[0]+args[1]

def parameter_init():
    return random.random()*0.5-10

number_of_parameters = 2
max_iterations = 100
best_parameters, best_error_score = particle_swarm_optimize(simple_error_function, number_of_parameters,
                                        max_iterations, weight_init, parameter_init=parameter_init)

• stopping_error: float = 0.001 # stop when we have a particle with this score
• num_particles: int = 10 # number of particles to run
• max_iterations_without_improvement: int = 10 # stop when we have this many consecutive turns without improvement
• c1: float = 2.0 # c1 PSO parameter
• c2: float = 2.0 # c1 PSO parameter