This is a collection of algorithms I studied at my university for the Evolutionary Computation course.

This repository includes 8 methods for function optimization:

• Genetic algorithm
• Evolutionary strategy
• Differential evolutionary
• Symbiotic optimization
• Artificial bee colony
• Deformed stars
• Fractal structurization
• Simulated Annealing

You can also find Genetic Programming (GP) algorithm and Ant Colony Optimization (ACO) algorithm with such variations:

• AS
• MMAS
• ACS

I heavily rely on NumPy (performing operations on n-dimensional arrays) and Matplotlib (data visualization), so you would need to install the dependencies first.

Clone this project and run:

pip install -r requirements.txt

You can run tests on the algorithm parameters. Depending on the algorithm, the parameters are different.

The example command could be this:

python main.py --algo symbiotic_optimization --parameter bf1

Or shorter version:

python main.py -a symbiotic_optimization -p bf1

This command will run a test on the Symbiotic Optimization Algorithm, testing different BF1 coefficients.

This repository includes some test functions for 1-, 2-, and n-dimensional cases. The following functions are included:

For the 2-dimensional case:

• Three Hump Camel
• Ackley
• Easom

For the n-dimensional case:

• Sphere

Adding your own function is simple. In src/lib/functions.py you would see Function class, which serves as an interface. To add your own function create a class and implement the Function interface.

Example of adding Booth function:

class Booth(Function):
    @property
    def bounds(self):
        return np.array([[-10.0, 10.0], [-10.0, 10.0]])

    def evaluate(self, solution: NDArray):
        x, y = solution
        return (x + 2*y - 7)**2 + (2*x + y - 5)**2

You can also pass some extra parameters to the program.

To create a graph:

python main.py -a symbiotic_optimization -p bf1 --plot

Output the results in Excel table or CSV file

# excel
python main.py -a symbiotic_optimization -p bf1 --output excel

# csv
python main.py -a symbiotic_optimization -p bf1 --output csv

To create a graph and save it in the results directory:

python main.py -a symbiotic_optimization -p bf1 --plot --save-plot

This project uses a YAML configuration file to set various parameters for different optimization algorithms.

The file consists of multiple sections, each representing an optimization algorithm. Each section consists of default parameters and parameters to test.

Consider the following configuration for Symbiotic Optimization algorithm:

symbiotic_optimisation:
  default:
    bf1: 1
    bf2: 2
  bf1: [1, 2]
  bf2: [1, 2]

Running the command python main.py -a symbiotic_optimization -p bf1 will initiate the Symbiotic Optimization algorithm, preloading default values for all parameters except bf1. In this context, bf1 will encompass an array of test values, specifically [1, 2].

The program in the "delivery" directory is designed to manage and optimize logistics operations by utilizing the ACO algorithm. It reads data about parcels, couriers, and distances, and then efficiently assigns parcels to couriers based on courier type and their sector of operation. It also calculates the minimum delivery cost, considering variables such as courier salary, driving and walking speeds, and fuel costs. The output provides details of parcel-courier assignments, each courier's delivery costs, and the total cost for all deliveries

The script divides the given area into four sector based on x and y coordinates with the delivery hub at the center. Couriers are then assigned to their respective sectors. Parcels are assigned to couriers who have capacity in terms of volume and weight in each sector.

For couriers who have more than 1 assigned parcel, Ant Colony Optimization is used to solve the Traveling Salesman Problem (TSP), intending to minimize the delivery cost. For couriers with only one parcel, the delivery cost derives straightforward from the distance.

Prior to executing the program, it is necessary to generate the required data. The script responsible for generating said data is located in the scripts directory. To begin, ensure that the script is executable by running the following command:

chmod +x ./scripts/delivery_data.py

Then execute the script:

./scripts/delivery_data.py

Data will be generated in the delivery-data directory.

You can run the program with this command:

python -m delivery