Localization Using DV-hop Algorithm Enhanced with Particle Swarm Optimization and Genetic Algorithm Approaches

Localization methods utilizing particle swarm optimization (PSO), PSO-enhanced, and genetic algorithm (GA) improved DV-hop algorithms have achieved excellent results in practical applications. The particle swarm optimization algorithm effectively enhances node localization accuracy in DV-hop by iteratively updating particle positions and velocities based on fitness evaluations. The PSO algorithm significantly improves convergence speed through social and cognitive component adjustments in the optimization process. Meanwhile, genetic algorithm optimization further refines path selection and hop-count determination in DV-hop through selection, crossover, and mutation operations, resulting in more accurate and reliable localization outcomes. These algorithms can be implemented with key functions such as fitness calculation for position evaluation, velocity updates using inertia weights, and chromosome encoding for hop-count optimization.