Efficient Shortest Path Finding Using Chaotic Fly Consonance Optimization Algorithm
Keywords:
Chaotic Fly Consonance Optimization, Deep Deterministic Policy Gradient, Efficient Path Finding, Traffic Flow.Abstract
This study presents an effective method for determining the shortest route for vehicles in urban traffic situations by combining the Chaotic Fly Consonance Optimization (CFCO) algorithm with the Deep Deterministic Policy Gradient (DDPG) algorithm for traffic flow estimation. By taking into account real-time traffic conditions and optimizing routes appropriately, the technique attempts to improve the overall performance of vehicle navigation systems. In the first stage, the system receives input in the form of traffic signal pictures ranging from 0 to 10, as well as the planned destination route. Following that, the deep deterministic policy gradient (DDPG) method is used to evaluate traffic flow, taking use of its model-free off-policy nature for continuous action reinforcement learning. This stage entails fine-tuning hyperparameter to produce the best possible outcomes in anticipating traffic conditions. Following the assessment of traffic flow, the suggested chaotic fly consonance optimization (CFCO) method is used in the third stage to discover the vehicle's shortest route. The CFCO method is used for global optimization and was inspired by the chaotic behavior of flies in nature. This stage entails using the algorithm's chaotic nature to effectively search the solution space and locate optimum pathways. The last phase displays the shortest route found by combining DDPG for traffic flow assessment and CFCO for path optimization. The approach is assessed based on its capacity to adapt to changing traffic circumstances and propose effective routes, eventually leading to better vehicle navigation in metropolitan areas.
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