• Implement of this article and try to improve each model and embedding (Article).
• Optimizes emergency exit placement for pedestrian evacuation on grid-based environments by simulating crowd
flow and minimizing a composite evacuation fitness objective across multiple pedestrian configurations.
• Uses a cellular automaton evacuation engine with static/dynamic floor fields, stochastic movement, and von
Neumann/Moore neighborhoods to model pedestrian dynamics and exits.
• Implements multiple exit-optimization strategies: evolutionary algorithm (EA), improved EA (IEA), greedy,
memetic search, Q-learning, Grey Wolf Optimizer (GWO), and MISO/GA hybrids selectable via CLI.