Zombie Survival Optimization: A swarm intelligence algorithm inspired by zombie foraging

摘要

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 approach modeled after the foraging behavior of zombies. Zombies (exploration agents) search in a space where the underlying fitness is modeled as a hypothetical airborne 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 algorithm is useful for search, such as searching an image for a pedestrian. Experiments on the CAVIAR dataset suggest improved efficiency over Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). A C++ implementation is available.