Simulated Annealing is a probabilistic metaheuristic optimization algorithm inspired by the physical annealing process in metallurgy, which iteratively explores a solution space by accepting both improving and, with a controlled probability that decreases over time, worsening candidate solutions to escape local optima. In WiFi/CSI and indoor sensing research, it is employed to solve complex, non-convex optimization problems such as beacon placement for coverage maximization and parameter estimation in crowd simulation models, where exhaustive search is computationally intractable. Key variants include adaptive cooling schedules and hybrid formulations that combine Simulated Annealing with greedy or incremental strategies, as seen in beacon deployment frameworks, to balance solution quality with computational efficiency.
Source Papers
- Efficient Adaptive Beacon Deployment Optimization for Indoor Crowd Monitoring Applications ↗ — Efficient Adaptive Beacon Deployment Optimization for Indoor
- Parameter estimation and comparative evaluation of crowd simulations ↗ — Parameter estimation and comparative evaluation of crowd sim
- Social force models for pedestrian traffic – state of the art ↗ — Social force models for pedestrian traffic – state of the ar