Zombie Survival Optimization: A Swarm Intelligence Algorithm Inspired By Zombie Foraging
Hoang Thanh Nguyen, Bir Bhanu · 2015
Search optimization algorithms have the challenge of balancing between exploration of the search space (e.g., map locations, image pixels) and exploitation of learned information (e.g., prior knowledge, regions of high fitness). To address this challenge, we present a very basic framework which we call Zombie Survival Optimization (ZSO), a novel swarm intelligence ap-proach modeled after the foraging behavior of zombies. Zombies (exploration agents) search in a space where the underlying fitness is modeled as a hypothetical air-borne antidote which cures a zombie’s aliments and turns them back into humans (who attempt to survive by exploiting the search space). Such an optimization al-gorithm is useful for search, such as searching an image for a pedestrian. Experiments on the CAVIAR dataset suggest improved efficiency over Particle Swarm Op-timization (PSO) and Bacterial Foraging Optimization (BFO). A C++ implementation is available. 1