Optimizations on n-dimensional spaces with particle swarms
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ndopt — Optimization on n-dimensional functions

ndopt applies the particle swarm algorithm to find the global minima in a set of mathematical functions.



ndopt uses numpy out of convenience to compute the mathematical operations (not sure if it's really necessary and maybe everything could be found in the math module, but numpy is so much more fun !)

It also uses matplotlib for the graphical representation of the functions in 3D, especially the mplot3d module.

There are several parameters that can be passed to the script:

  • dim is the number of dimensions on which you want to run the optimization. Keep in mind that the higher the dimension, the harder the optimization problem.
  • function is the name of a function to optimize. All supported function can be found in the functions.py file. The value passed must match the class name, and the case doesn't matter. For example, to run optimization on the Zakharov function, you can see that there is a class called Zakharov so you could pass in a string like zakharov.
  • num-particles is the number of particles you want to use for your optimization. Defaults to 25.
  • iterations is the maximum number of iterations after which the algorithm will stop if it has not converged. Avoid infinite loops.
  • verbose is the verbose level which is a positive number more than or equal to 0. The higher the verbose, the more details you will see. At 0 there will be no details, 1 will display the initial and final functions, and 2 will display the functions at each iteration.
  • phi is the initial speed variation coefficient.
  • K is the mobility of the particles.

For a complete list of all parameters available, you can use the help menu:

python particle_swarm -h
Usage: particle_swarm [options]

  -h, --help            show this help message and exit
  -d DIM, --dim=DIM     dimension of the function to optimize
  -f FUNCTION, --function=FUNCTION
                        name of the function to optimize
  -n PARTICLES, --num-particles=PARTICLES
                        number of particles in the swarm
  -i ITERATIONS, --iterations=ITERATIONS
                        max number of iterations
  -v VERBOSE, --verbose=VERBOSE
                        verbose level
  -p PHI, --phi=PHI     initial speed variation coefficient
  -k K                  mobility of the particles


The results of the particle swarm algorithm will be described on each of the functions supported in the functions.py file. These functions have very different properties and topology, so it is a good indication to see how well the particle swarm algorithm performs.


The Rosenbrock function is a non-convex function whose minima is found in a parabolic valley.

It is defined by:


Applying particle swarm gives us the minima in (1, 1) like shown below:


Goldstein and Price

The Goldstein and Price function has a minima in (0, -1):




De Jong 1


De Jong 2

The De Jong 2 function is just another name for the Rosenbrock function.

De Jong 3










Ackley Path



There are 3 important parameters to analyze to see how the particle swarm performs

  • number of particles : how does performance behave as we increase the number of particles?
  • initial speed variation coefficient : this parameter is tied to the inner workings of the particles behavior, and changing this will greatly affect how the algorithm behave.
  • mobility of the particles : if particles are more mobile, we will see different properties of the algorithm.

Influence of the number of particles

The following diagram takes the Michalewicz function as an example and shows 1 curve for different number of particles used in the algorithm. Here is some observations we can make:

  • with 10 particles, parts of the solution space won't be examined, and so we are bound to converge towards a local minima where the particles will be trapped.
  • with 25 particles, the solution space is examined more widely, but we are still not converging towards the global minima.
  • with 50 particles, it takes more time to converge, but the value towards which we converge is closer to the global minima.


These observations make sense with the intuition, so it's important to use a reasonably high number of particles if we want to get closer to the global minima.

Influence of the initial speed variation coefficient

From the diagram below, we are testing different values of phi to see how quickly we converge towards the solution.

We can see that with too large values of phi, it takes a very long time to converge. In practice, a value of 4 is a good choice. In some cases, using large values can cause the algorithm to diverge because the particles will constantly overshoot the mean at every cycle.


Influence of the mobility of the particles

In the diagram below, we can clearly see that by setting K too small, the particles will be stuck in a local minima and unable to get out, while using a large value of K will fail to converge. It is important to take a good compromise between speed of convergence and accuracy.