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High-performance Evolutionary TSP Solver with Gamified Streamlit UI. Features Hybrid GA (Genetic Algorithm) + 3-Opt Local Search, TSPLIB support (EUC_2D/ATT), and real-time visualization.
Genetic Algorithm solver for the Traveling Salesman Problem (TSP) with configurable operators — tournament selection, order crossover, swap/inverse mutation. Includes greedy baseline and TSPLIB benchmark datasets.
This project implements two nature-inspired optimization algorithms: Moth Flame Optimization (MFO) and Honey Badger Optimization (HBO). Both algorithms are designed to solve complex optimization problems by mimicking behaviors observed in nature. also it includes a path finding algorithm, A-star