Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Area of Science:

• Vehicular Ad Hoc Networks (VANETs)
• Optimization Algorithms
• Engineering Applications

Background:

• Vehicular ad hoc networks (VANETs) require continuous topology change identification for reliable routing protocols.
• Optimal configuration of these protocols is challenging without intelligent design tools.
• Metaheuristic techniques offer suitable solutions for complex optimization problems.

Purpose of the Study:

• To propose and evaluate novel metaheuristic algorithms for optimizing VANET routing protocols.
• To introduce Glowworm Swarm Optimization (GSO), Simulated Annealing (SA), and a hybrid SA-GSO algorithm.
• To address challenges in efficient protocol configuration and constrained engineering problems.

Main Methods:

• Implementation of Glowworm Swarm Optimization (GSO) for fast convergence to feasible regions.
• Application of Simulated Annealing (SA) as an optimization method mimicking thermal system freezing.
• Development of a hybrid slow heat SA-GSO algorithm incorporating a local search strategy based on SA to prevent premature convergence.

Main Results:

• The proposed SA-GSO algorithm demonstrates a faster speed of convergence compared to existing methods.
• The hybrid algorithm achieves higher precision in computational tasks.
• Effectiveness in solving constrained engineering problems, including routing and heat transfer.

Conclusions:

• The hybrid slow heat SA-GSO algorithm is a highly effective approach for optimizing VANET routing protocols.
• This method offers improved performance in terms of speed and accuracy for complex engineering challenges.
• Intelligent design tools and metaheuristic techniques are crucial for advancing VANET efficiency.