Optimizing Ant Food Collection Using DEAP Algorithm in Python Project

This project focuses on optimizing ant food collection using the DEAP (Distributed Evolutionary Algorithms in Python) algorithm. The implementation is done in Python, utilizing genetic algorithms for object detection and position estimation. The project explores the application of artificial life principles, drawing parallels to the behavior of ants in nature. The document includes a literature survey, detailing relevant concepts such as Ant Colony Optimization (ACO) and its variants. The core of the project involves using the DEAP framework to create and evaluate solutions, with screenshots provided to illustrate the implementation. The project highlights the motivations behind using genetic algorithms and DEAP for this specific problem, emphasizing the suitability of these techniques for simulating and optimizing the ant food collection process. The project showcases the optimization process through generations, with the green particles (ants) moving randomly to locate the blue food particles, culminating in optimized results after multiple iterations. The project also highlights the benefits of DEAP, such as its explicit algorithm design and transparent data structures, making it ideal for evolutionary computation.