Project written for Applied Computer Science Computational intelligence methods course at AGH UST WFiIS.
Semester 6, year 2022.
The exercise of this project is to use Fuzzy Logic system to classify IRIS, WINE and SEEDS datasets found on UC MLR archive. To improve the resulting classification success rate, the project includes an optimization algorithm.
Novel efficient meta-heuristic optimization algorithm called Colliding Bodies Optimization (CBO) is based on one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass. After a collision of two moving bodies having specified masses and velocities, these bodies are separated with new velocities. This collision causes the agents to move toward better positions in the search space. CBO utilizes simple formulation to find minimum or maximum of functions and does not depend on any internal parameter.
Source: This paper
This project is licensed under MIT, a free and open-source license. For more information, please see the license file.
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Iris: http://archive.ics.uci.edu/ml/datasets/Iris
Seeds: http://archive.ics.uci.edu/ml/datasets/seeds
Wine: http://archive.ics.uci.edu/ml/datasets/Wine