Heuristic Optimization for Python
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SwarmOps - Heuristic Optimization for Python.

SwarmOps for Python implements the following heuristic optimizers which do not use the gradient of the problem being optimized:

  • Particle Swarm Optimization (PSO)
  • Differential Evolution (DE)
  • Many Optimizing Liaisons (MOL) - A simple variant of PSO
  • Pattern Search (PS)
  • Local Unimodal Sampling (LUS)

All these optimizers perform minimization. If you need to maximize a problem then you simply return the negated value in your fitness function (aka. cost- or error-function).

The PS and LUS optimizers only require few iterations and are therefore useful for problems that are very time-consuming to compute. PSO, MOL and DE usually require many more iterations but can also sometimes find better solutions.

Multiple optimization runs can be performed using the MultiRun-class, which also supports printing various statistics, plotting a trace of the fitness, and refining the best-found solution using the L-BFGS-B optimizer from SciPy.

Parallel execution is also supported in different ways, see below.

Meta-optimization is also supported, where the control / behavioural parameters of an optimizer may be tuned by using an overlaying meta-optimizer, see below.


The easiest way to install SwarmOps and use it in your own project, is to use a virtual environment. You write the following in a Linux terminal:

mkdir ~/dev/my-project/
cd ~/dev/my-project/
virtualenv swarmops-env
source activate swarmops-env/
pip install swarmops

You can also use Anaconda instead of a virtualenv:

conda create --name swarmops-env python=3
source activate swarmops-env
pip install swarmops

If the last command fails then you can try the following instead:

pip install git+https://github.com/Hvass-Labs/swarmops.git

When you are done working on the project you can deactivate the virtualenv:

source deactivate


You can now import SwarmOps when the swarmops-env virtual environment is active.

For example, you can make a file called ~/dev/my-project/test.py with the following content:

from swarmops.DE import DE
from swarmops.Problem import Rosenbrock


problem = Rosenbrock(dim=3)

result = DE(problem=problem, max_evaluations=10000)


This is supposed to execute a single optimization run using Differential Evolution (DE) on the Rosenbrock problem in 3 dimensions, using 10000 fitness evaluations.

You can run the program by entering:

python test.py

The output should be something like this (the numbers may be somewhat different because they are random):

Differential Evolution (DE/Rand/1/Bin)
[ 1.00000295  1.00000462  1.00000635]


If you want to modify the source-code or use the provided demo scripts, then you can download it from GitHub as a Zip-file and unpack it into a directory of your choice.

Or you can download it using git by typing the following in a Linux terminal:

git clone https://github.com/Hvass-Labs/swarmops.git

This creates a directory named swarmops which contains all the source-code.


SwarmOps requires NumPy. Additionally, if you want to use L-BFGS-B to refine the best-found solution, then you also need SciPy. And if you want to plot the fitness-trace, then you also need matplotlib. These are installed automatically when using pip install described above.

SwarmOps was developed using Anaconda but it should also work with other Python installations as long as you have at least NumPy installed, and SciPy and matplotlib if you require them.

However, the parallel execution apparently does not work with Python 2.7, so you must use Python 3.X if you want to use parallel execution in SwarmOps.

Optimization Demo

A short demo for optimizing benchmark problems is found in demo-optimize.py The easiest way to learn how to use SwarmOps for optimizing a problem is to study and modify this file. The source-code is generally well-documented.

To execute the file, type the following in a Linux terminal in the directory where you installed the swarmops source-code from GitHub:

python demo-optimize.py

Meta-Optimization Demo

Meta-optimization is the tuning of an optimizer's control / behavioural parameters by using another overlaying optimizer. This may greatly improve optimization performance. SwarmOps supports meta-optimization and a short demo is found in the file demo-meta-optimize.py

To execute the file, type the following in a Linux terminal in the directory where you installed swarmops:

python demo-meta-optimize.py


SwarmOps works for real-valued optimization problems, that is, problems that take as input some array x of floating point values and returns a single floating point value for the fitness associated with x. The goal is to find the array x that has the lowest fitness value.

The array x is sometimes called a candidate solution, or a position in the search-space. The fitness function is sometimes called a cost or error function.

You can implement your own optimization problem in two ways. Either you sub-class the Problem-class and override the fitness() function in that class, or you pass your fitness-function to the init-method of the Problem-class. This is described in more detail in the file Problem.py and an example is given in demo-optimize.py

Unit Tests

Unit-tests / nose-tests are found in the file test_all.py which test the optimizers with thousands of different configurations. The purpose is to test whether exceptions are raised somewhere and if the output is of the expected data-type. The optimization results are not tested for correctness because these optimizers are stochastic in nature and will always give different results. This is discussed in more detail in the test_all.py file. This means you should also manually inspect the output of demo-optimize.py and demo-meta-optimize.py so as to assess whether the optimization results are satisfactory.

Parallel Execution

SwarmOps supports parallel execution in 3 ways depending on your needs:

  1. The MultiRun-class can execute several optimization runs in parallel. This is usually the best way because the overhead of the parallel execution is very low so there is a near-linear speed-up depending on the number of CPU cores. However, if you only want to perform a single optimization run then you cannot use this way of making the execution parallel.

  2. The optimizers PSO, MOL and DE can execute in parallel. This is done by running the fitness evaluations in parallel for each iteration of the optimizer. However, the overhead is significant, especially on Windows, so this way of doing parallel execution should only be used if (A) you only want to perform a single optimization run, and (B) your fitness function is slow to compute.

  3. You can make your fitness function execute in parallel by yourself and then use the optimizers from SwarmOps in non-parallel mode.

Parallel execution apparently does not work in Python 2.7, please use Python 3.x.

Parallel Meta-Optimization

SwarmOps supports running meta-optimization in parallel with some limitations. The list of the best control parameters found during meta-optimization will be empty. Similarly the best-found solutions for the optimization problems will also be empty.

If these results are important to you, then you should run meta-optimization in non-parallel mode, and instead run several instances of your program at the same time. Then you will get parallel execution (assuming your CPU has multiple cores) and you will still get the list of the best-found control parameters and solutions to the problems.

This design choice was made because Python's parallel implementation is a bit peculiar and it would have required significant changes to MetaOptimize.py to make it work, which would have made the source-code much more complicated, while the work-around is quite simple.


SwarmOps for Python only implements boundary constraints. If this is insufficient for your needs, then a suggestion is to 'repair' the candidate solutions before you calculate the fitness of a problem. For example, if your fitness function is called with x, and you must ensure that the elements of x sum to one, then you would simply 'repair' x inside your fitness function by calculating z = x / np.sum(x) and then calculating the fitness for z instead of x, and use a special case if x sums to zero.

SwarmOps for C# supports more general constraints.

Convergence Tests

SwarmOps does not implement convergence tests. The reason is that the optimizers may appear to have stagnated for hundreds or even thousands of iterations, and then suddenly they find an improvement. Instead you should set the max number of fitness evaluations and optimization runs that you can afford to run in the available time.

Other Programming Languages

SwarmOps is also implemented in other programming languages such as C#, C, Java, and Matlab. Especially the C# and Java versions have more features than the Python version and will execute much faster for meta-optimization.



Copyright (C) 2003-2016 Magnus Erik Hvass Pedersen. See LICENSE.txt for details.

Version History

V. 1.0 - First release (April 12, 2016)