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Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experimental results can be evaluated in various ways, including diagrams, tables, and export to Excel.
Two rival teams of intelligent virtual agents with different abilities compete to gather specific resources from their shared environment. Implemented in Godot, the simulation utilizes A* algorithm, DFS, and genetic algorithms. The team that collects the resources first wins the game.
This is a genetic algorithm example in which we use OpenAI gym environment and use its cartpole function. The fitness values for each generation improves though genetic algorithm. Problem statement and output graphs provided.
This project involves the analysis and comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for minimizing mathematical functions and solving the Traveling Salesman Problem (TSP) using the scikit-opt library. Both GA and PSO are popular metaheuristic algorithms used for optimization problems.