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CA-ICA Traffic Optimization is a smart transportation research project that applies Cultural Algorithm (CA) and Imperialist Competitive Algorithm (ICA) to optimize urban traffic systems, public transportation networks, and bus allocation through AI-driven simulation models.
This project focuses on Adaptive University Timetabling Optimization using a hybrid approach combining Genetic Algorithm (GA) and genetic algorithm variation like memetic and cultural simulated annealing algorithm artificial bee clony ABC algorithm Particle Swarm Optimization (PSO).
-An AI optimization engine solving the complex Job Scheduling Problem (JSP). Implements Backtracking Search and Cultural Algorithms with a full-featured Python GUI, strict constraint handling, and Matplotlib-driven comparative analytics.