Indago 0.0.6

Numerical optimization framework

Indago

Indago is a Python 3 module for numerical optimization.

Installation

For easiest install for using the optimization framework use

pip3 install indago

In order to obtain Indago code, clone Gitlab repository by executing following command in the directory where you want to loacte Indago root directory:

git clone https://gitlab.com/sivic/indago.git

For building and installing Indago package into your Python environment

python setup.py build
python setup.py install

Or for continous testing/developing:

python setup.py clean build install

Dependencies

A following packages should be installed using aptitude:

• python3
• python3-pip
• python3-tk

    sudo apt install python3 python3-pip python3-tk

After packages installation using above command, additional python packages should be installed using pip from requirements.txt

    pip install -r requirements.txt

Algorithms

Indago is a Python module for numerical optimization of real fitness function over a real parameter domain. It was developed at the Department for Fluid Mechanics and Computational Engineering of the University of Rijeka, Faculty of Engineering, by Stefan Ivić, Siniša Družeta, and others.

Indago is developed for in-house research and teaching purposes and is not officially supported in any way, it is not properly documented and probably needs more testing. But hey, we use it, it works for us, and it's free! Anyway, proceed with caution, as you would with any other beta-level software.

As of now, Indago consists of three stochastic swarm-based optimizers, namely Particle Swarm Optimization (PSO), Fireworks Algorithm (FWA) and Squirrel Search Algorithm (SSA). They are all available through the same API, which was designed to be as accessible as possible. Indago relies heavily on NumPy, so the inputs and outputs of the optimizers are mostly NumPy arrays. Besides NumPy and a couple of other stuff here and there (such as a few SciPy functions), Indago is pure Python. Indago optimizers also include some of our original research improvements, so feel free to try those as well. And don't forget to cite. :)

Particle swarm optimization

Using Indago is easy. Let us use PSO as an example. First, we need to import NumPy and Indago PSO, and then initialize an optimizer object:

import numpy as np
from indago.pso import PSO
pso = PSO()

Then, we must provide a goal function which needs to be minimized, say:

def goalfun(x):	# must take 1D np.array
    return np.sum(x**2) # must return scalar number
pso.evaluation_function = goalfun

Now we can define optimizer inputs:

pso.method = 'Vanilla' # we will use standard PSO, the other available option is 'TVAC' [1]
pso.dimensions = 20 # number of variables in the design vector (x)
pso.swarm_size = 15 # number of PSO particles
pso.iterations = int(1000 * pso.dimensions / pso.swarm_size) # any integer will do, but 10³D function calls is possibly a good choice
pso.lb = np.ones(pso.dimensions) * -1 # 1D np.array of lower bound values
pso.ub = np.ones(pso.dimensions) * 1 # 1D np.array of lower bound values

Also, we must provide optimization method parameters:

pso.params['cognitive_rate'] = 1.0 # PSO parameter also known as c1 (ranges from 0.0 to 2.0)
pso.params['social_rate'] = 1.0 # PSO parameter also known as c2 (ranges from 0.0 to 2.0)
pso.params['inertia'] = 0.72 # PSO parameter known as inertia weight (w, ranges from 0.5 to 1.0), the other available options are 'LDIW' (w linearly decreasing from 1.0 to 0.4) and 'anakatabatic'

If we want to use our (still unpublished) adaptive inertia weight technique:

pso.params['inertia'] = 'anakatabatic'

then we need to also specify the anakatabatic model:

pso.params['akb_model'] = 'languid' # [2,3], the other options are 'fish' and 'nosedive'

Finally, we can start the optimization and get the results:

result = pso.run()
min_f = result.f # fitness at minimum, scalar number
x_min = result.x # design vector at minimum, 1D np.array

And that's it!

Fireworks algorithm

If we wanted to use FWA [4], we just have to import it instead of PSO:

from indago.pso import FWA
fwa = FWA()

Now we can proceed in the same manner as with PSO. For FWA, the only method available is basic FWA:

fwa.method = 'Vanilla'

In FWA, we do not need swarm_size parameter and we have to set the following method parameters:

fwa.params['n'] = 20 # DA LI JE OVO U BITI SWARM_SIZE?
fwa.params['m1'] = 10
fwa.params['m2'] = 10

Lastly, if we want to try our luck with SSA [5], we initialize it like this:

from indago.pso import SSA
ssa = SSA()

In SSA, the only available method is 'Vanilla', and we need to provide the swarm_size parameter. Also, there is only one mandatory method parameter:

ssa.params['acorn_tree_attraction'] = 0.5 # ranges from 0.0 to 1.0

Optionally, we can define a few other parameters:

ssa.params['predator_presence_probability'] = 0.1
ssa.params['gliding_constant'] = 1.9 
ssa.params['gliding_distance_limits'] = [0.5, 1.11] 

Among other stuff, Indago also includes the CEC2014 test suite [6], comprising 30 test functions for real optimization methods. You can use it by importing it like this:

from indago.benchmarks import CEC2014

Then, you have to initialize it for a specific dimensionality of the test functions:

test = CEC2014(10) # you can also use 20, 50 and 100

Now you can use specific test functions (test.F1(), test.F2(), ...up to test.F30()), they all take 1D np.array of size 10/20/50/100 and return a scalar number. Alternatively, you can iterate through the built-in list of them all:

test_results = []
for f in test.functions:
    optimizer.evaluation_function = f
    test_results.append(optimizer.run().f)

Have fun!

References:

1. Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on evolutionary computation, 8(3), 240-255.

2. Družeta, S., & Ivić, S. (2017). Examination of benefits of personal fitness improvement dependent inertia for Particle Swarm Optimization. Soft Computing, 21(12), 3387-3400.

3. Družeta, S., Ivić, S., Grbčić, L., & Lučin, I. (2019). Introducing languid particle dynamics to a selection of PSO variants. Egyptian Informatics Journal.

4. Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and evolutionary computation, 44, 148-175.

5. Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2013). Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635.