# Particle Swarm Optimization Basics¶

The implementation presented here is the original PSO algorithm as presented in [Poli2007]. From Wikipedia definition of PSO

PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae. The movements of the particles are guided by the best found positions in the search-space which are updated as better positions are found by the particles.

## Modules¶

Before writing functions and algorithms, we need to import some module from the standard library and from DEAP.

```
import operator
import random
import numpy
import math
from deap import base
from deap import benchmarks
from deap import creator
```

## Representation¶

The particle’s goal is to maximize the return value of the function at its position.

PSO particles are essentially described as positions in a search-space of D dimensions. Each particle also has a vector representing the speed of the particle in each dimension. Finally, each particle keeps a reference to the best state in which it has been so far.

This translates in DEAP by the following two lines of code :

```
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Particle", list, fitness=creator.FitnessMax, speed=list,
smin=None, smax=None, best=None)
```

Here we create two new objects in the `creator`

space. First, we
create a `FitnessMax`

object, and we specify the
`weights`

to be `(1.0,)`

, this means we want to
maximise the value of the fitness of our particles. The second object we
create represent our particle. We defined it as a `list`

to which we
add five attributes. The first attribute is the fitness of the particle, the
second is the speed of the particle which is also going to be a list, the
third and fourth are the limit of the speed value, and the fifth attribute
will be a reference to a copy of the best state the particle has been so far.
Since the particle has no final state until it has been evaluated, the
reference is set to `None`

. The speed limits are also set to `None`

to
allow configuration via the function `generate()`

presented in the next
section.

## Operators¶

PSO original algorithm uses three operators : initializer, updater and
evaluator. The initialization consist in generating a random position and a
random speed for a particle. The next function creates a particle and
initializes its attributes, except for the attribute `best`

, which will
be set only after evaluation :

```
def generate(size, pmin, pmax, smin, smax):
part = creator.Particle(random.uniform(pmin, pmax) for _ in range(size))
part.speed = [random.uniform(smin, smax) for _ in range(size)]
part.smin = smin
part.smax = smax
return part
```

The function `updateParticle()`

first computes the speed, then limits the
speed values between `smin`

and `smax`

, and finally computes the new
particle position.

```
def updateParticle(part, best, phi1, phi2):
u1 = (random.uniform(0, phi1) for _ in range(len(part)))
u2 = (random.uniform(0, phi2) for _ in range(len(part)))
v_u1 = map(operator.mul, u1, map(operator.sub, part.best, part))
v_u2 = map(operator.mul, u2, map(operator.sub, best, part))
part.speed = list(map(operator.add, part.speed, map(operator.add, v_u1, v_u2)))
for i, speed in enumerate(part.speed):
if abs(speed) < part.smin:
part.speed[i] = math.copysign(part.smin, speed)
elif abs(speed) > part.smax:
part.speed[i] = math.copysign(part.smax, speed)
part[:] = list(map(operator.add, part, part.speed))
```

The operators are registered in the toolbox with their parameters. The
particle value at the beginning are in the range `[-100, 100]`

(`pmin`

and `pmax`

), and the speed is limited in the range
`[-50, 50]`

through all the evolution.

The evaluation function `h1()`

is from [Knoek2003]. The
function is already defined in the benchmarks module, so we can register it
directly.

```
toolbox = base.Toolbox()
toolbox.register("particle", generate, size=2, pmin=-6, pmax=6, smin=-3, smax=3)
toolbox.register("population", tools.initRepeat, list, toolbox.particle)
toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0)
toolbox.register("evaluate", benchmarks.h1)
```

## Algorithm¶

Once the operators are registered in the toolbox, we can fire up the algorithm by firstly creating a new population, and then apply the original PSO algorithm. The variable best contains the best particle ever found (it is known as gbest in the original algorithm).

```
def main():
pop = toolbox.population(n=5)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = ["gen", "evals"] + stats.fields
GEN = 1000
best = None
for g in range(GEN):
for part in pop:
part.fitness.values = toolbox.evaluate(part)
if not part.best or part.best.fitness < part.fitness:
part.best = creator.Particle(part)
part.best.fitness.values = part.fitness.values
if not best or best.fitness < part.fitness:
best = creator.Particle(part)
best.fitness.values = part.fitness.values
for part in pop:
toolbox.update(part, best)
# Gather all the fitnesses in one list and print the stats
logbook.record(gen=g, evals=len(pop), **stats.compile(pop))
print(logbook.stream)
return pop, logbook, best
```

## Conclusion¶

The full PSO basic example can be found here : examples/%spso/basic.

This is a video of the algorithm in action, plotted with matplotlib. The red dot represents the best solution found so far.

## References¶

[Poli2007] | Ricardo Poli, James Kennedy and Tim Blackwell, “Particle swarm optimization an overview”. Swarm Intelligence. 2007; 1: 33–57 |

[Knoek2003] | Arthur J. Knoek van Soest and L. J. R. Richard Casius, “The merits of a parallel genetic algorithm in solving hard optimization problems”. Journal of Biomechanical Engineering. 2003; 125: 141–146 |