# Using PSO to minimise the Michalewicz function

This tutorial showcases how to use the built-in Particle Swarm Optimisation (PSO) algorithm for finding the minimum in a continuos setting.

We start by importing our necessary modules

using EvoLP
using Statistics
using OrderedCollections

For this example, we will use the Michalewicz function, which is a test function included in EvoLP:

@doc michalewicz
michalewicz(x; m=10)

The Michalewicz function is a d-dimensional function with several steep valleys, where m controls the steepness. m is usually set at 10. For 2 dimensions, $x^* = [2.20, 1.57]$, with $f(x^*) = -1.8011$.

$f(x) = -\sum_{i=1}^{d}\sin(x_i) \sin^{2m}\left(\frac{ix_i^2}{\pi}\right)$

In this case we will use d=2 and m=10, which are the default values implemented.

In PSO, we use particles. Each particle has a position and a velocity, and remembers the best position the whole swarm has visited. We can create a population of particles in multiple ways, but EvoLP provides 2 particle generators with random positions: either uniform or following a normal distribution.

Let's use the normal generator:particle generators

@doc normal_rand_particle_pop
normal_rand_particle_pop(n, μ, Σ; rng=Random.GLOBAL_RNG)

Generate a population of n Particle using a normal distribution with means μ and covarianceΣ. μ expects a vector of length l (i.e. number of dimensions) while Σ expects an l x l matrix of covariances.

Since we are using the 2-dimensional version of the function, we need to provide a vector of 2 means and a 2 \times 2 matrix of covariances:

population = normal_rand_particle_pop(50, [0, 0], [1 0; 0 1])
first(population, 3)
3-element Vector{Particle}:
Particle([0.4040308336567521, -1.0956708260389603], [0.0, 0.0], Inf, [0.4040308336567521, -1.0956708260389603], Inf)
Particle([-0.5690754175927688, -1.9016076908148127], [0.0, 0.0], Inf, [-0.5690754175927688, -1.9016076908148127], Inf)
Particle([0.9895469924697422, -1.2022337920748436], [0.0, 0.0], Inf, [0.9895469924697422, -1.2022337920748436], Inf)

We can use the Logbook to save information about each iteration of the run. Let's save the average, median and best fitness:

statnames = ["avg_fit", "median_fit", "best_fit"]
callables = [mean, median, minimum]

thedict = LittleDict(statnames, callables)
logbook = Logbook(thedict)
Logbook(LittleDict{AbstractString, Function, Vector{AbstractString}, Vector{Function}}("avg_fit" => Statistics.mean, "median_fit" => Statistics.median, "best_fit" => minimum), NamedTuple{(:avg_fit, :median_fit, :best_fit)}[])

Now we can use the built-in PSO algorithm:

@doc PSO
PSO(f::Function, population::Vector{Particle}, k_max::Integer; w=1, c1=1, c2=1)
PSO(logger::Logbook, f::Function, population::Vector{Particle}, k_max::Integer; w=1, c1=1, c2=1)

Arguments

• f::Function: Objective function to minimise
• population: Population—a list of Particle individuals
• k_max: maximum iterations
• w: Inertia weight. Optional, by default 1.
• c1: Cognitive coefficient (my position). Optional, by default 1
• c2: Social coefficient (swarm position). Optional, by default 1

Returns a Result.

Let's use the default parameters, and 30 iterations:

results = PSO(logbook, michalewicz, population, 30);

The output was suppressed so that we can analyse each part of the result separately using the Result functions:

@show optimum(results)
@show optimizer(results)
@show iterations(results)
@show f_calls(results)
optimum(results) = -1.7987560672102798
optimizer(results) = [2.1935299266534836, 1.5760612400982805]
iterations(results) = 30
f_calls(results) = 1550

We can also take a look at the logbook's records and see how the calculated statistics changed throughout the run:

for (i, I) in enumerate(logbook.records)
print("it: $(i) with best_pos:$(I[3]) and avg_pos: \$(I[1]) \n")
end
it: 1 with best_pos: -0.7504688680623768 and avg_pos: 0.027014191784156233
it: 2 with best_pos: -0.7906765701486448 and avg_pos: -0.11592097787713364
it: 3 with best_pos: -0.8012991501612791 and avg_pos: -0.17770241942233952
it: 4 with best_pos: -0.7963864986732629 and avg_pos: -0.17023969225290894
it: 5 with best_pos: -0.8726099514277835 and avg_pos: -0.2928504362404555
it: 6 with best_pos: -1.1697700406204043 and avg_pos: -0.33670799217170105
it: 7 with best_pos: -1.6221334762010642 and avg_pos: -0.3834145274427395
it: 8 with best_pos: -0.9285497521300503 and avg_pos: -0.37119067568197167
it: 9 with best_pos: -1.7520089205738312 and avg_pos: -0.42151516296756136
it: 10 with best_pos: -1.587048845608525 and avg_pos: -0.3869242929292781
...
it: 27 with best_pos: -1.7971172748131834 and avg_pos: -0.4865895651379651
it: 28 with best_pos: -1.787184774482611 and avg_pos: -0.4625727297276257
it: 29 with best_pos: -1.7916306274124452 and avg_pos: -0.42381102968626794
it: 30 with best_pos: -1.77202650821346 and avg_pos: -0.46380678949496384