| Summary: | Traffic congestion is a pressing issue in urban areas, affecting both economic productivity and
quality of life. This report explores the use of metaheuristic algorithms to optimize traffic light
schedules, aiming to minimize delays and improve traffic flow. Various metaheuristic algorithms
are implemented and evaluated, including Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), and Differential Evolution (DE). The results demonstrate the effectiveness of metaheuristic
algorithms in addressing the Traffic Light Scheduling Problem (TLSP) and offer insights into their
practical application in real-world traffic management scenarios.
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