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A research toolkit for the Bees algorithm in Python

## pybees: Python optimization toolkit using the bees algorithm ## What is it?

pybees is a Python package for function optimization. It uses the nature inspired bees algorithm, proposed by Pham et al. and is built on top of SciPy. pybees is distributed under the 3-Clause BSD license.

The bees algorithm is a swarm based search algorithm, which mimics the food foraging behaviour of honey bees. The basic algorithm is suitable for both continuous and combinatorial optimization problems, which is demonstrated in the basic examples section.

## Main features

• High-level API to optimize continuous functions (training a multilayer perceptron).

• High-level API to optimize discrete functions (traveling salesperson problem).

• Built in single-objective cost functions.

• plotly plotting tools for 2D and 3D (both continuous and discrete).

• Extensible API for developing new ideas.

## Where to get it

If all dependencies are already installed, then the easiest way to install pybees is using `pip`.

```pip install pybees
```

## How to use it

Please visit the examples folder to view the demonstration examples. These include.

Future examples will include

• Multilayer perceptron

## Dependencies

pybees requires:

• numpy >= 1.17.4
• scipy >= 1.3.2
• plotly >= 4.4.1
• tqdm >= 4.40.2
• sklearn >= 0.22
• pandas >= 0.25.3

BSD 3

## Basic Examples

### Continuous function optimization

```import pybees as pb

sbc = pb.SimpleBeesContinuous(
n_scout_bees = 50,
elite_site_params = (15, 40),
best_site_params = (15, 30),
bounds = (-10,10),
n_dim = 2,
nbhd_radius = 2
)
```

This operation returns a `scipy.optimize.OptimizeResult`. `fun` represents the value of the objective function (lowest point). `nit` represents the number of iterations taken. `x` represents the coordinates of the value found for the objective function.

```>>> sbc.optimize(pb.levy)
fun: 1.007063464503951e-06
nit: 100
x: array([0.99905553, 0.99967304])
```

The results may also be visualised by using the following

```sbc.plot(global_min = (1, 1))
```

### Combinatorial optimization (e.g. travelling salesperson problem)

```import pybees as pb

sbd = pb.SimpleBeesDiscrete(
n_scout_bees = 50,
elite_site_params = (15, 40),
best_site_params = (15, 30),
coordinates = np.random.randint(10, size=[10, 2])
)
```

This operation returns a `scipy.optimize.OptimizeResult` result. `res.coordinates` represents `sbd.coordinates` reordered, resulting from the optimization of some objective function. In this example, `tour_distance` was minimized. As such, `coordinates` represents the sequence, corresponding to the shortest path between all coordinates (i.e. travelling salesperson problem). `fun` represents the value of the objective function (shortest distance). `nit` represents the number of iterations taken. `x` represents the permutation of the original sequence passed to `SimpleBeesDiscrete` which gives the shortest distance.

```>>> sbd.optimize(pb.tour_distance)
coordinates: array([
[7., 8.],
[4., 8.],
[2., 6.],
[0., 5.],
[1., 0.],
[3., 2.],
[2., 4.],
[3., 4.],
[5., 4.],
[7., 4.]])
fun: 27.228009718084742
nit: 100
x: array([1., 9., 7., 5., 0., 3., 4., 6., 2., 8.])
```

The results may also be visualised by using the following

```sbd.plot()
```

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