Modern urban transportation networks face persistent challenges of traffic congestion, inefficient route planning, and high operational costs. Classical optimization techniques struggle with large-scale Vehicle Routing Problems (VRP) because of their NP-hard nature. While quantum computers offer theoretical advantages for combinatorial optimization,current hardware limitations prevent their direct large-scale use. Quantum-inspired metaheuristic algorithms (e.g., Quantum Particle Swarm Optimizati
Develop a quantum-inspired metaheuristic optimization framework that dynamically generates near-optimal vehicle routes under real-time or simulated traffic conditions.The transportation network will be modelled as a...
Modern urban transportation networks face persistent challenges of traffic congestion, inefficient route planning, and high operational costs Classical optimization techniques struggle with large-scale Vehicle Routing Problems (VRP) because of their NP-hard nature While quantum computers offer theoretical advantages for combinatorial optimization,current hardware limitations prevent their direct large-scale use
Develop a quantum-inspired metaheuristic optimization framework that dynamically generates near-optimal vehicle routes under real-time or simulated traffic conditions.The transportation network will be modelled as a weighted graph. The framework will focus on algorithms such as Quantum Particle Swarm Optimization (QPSO) and will be benchmarked against conventional metaheuristics and exact methods. Objectives 1. Design a quantum-inspired metaheuristic framework that can solving large-scale VRP and shortest-path problems. 2. Minimize total travel time, distance, and traffic congestion. 3. Reduce computational complexity while improving convergence speed and solution quality compared with classical algorithms. 4. Demonstrate scalability for smart-city logistics and intelligent transportatio