Particle Swarm Optimization (PSO) and Bacteria Foraging Optimization (BFO) are
both heuristic optimization methods that belong to a class of algorithms that are known as
biomimetic. PSO is a population-based, stochastic method that models the behavior of swarms of
insects and birds as they search for food. BFO simulates the process of bacteria searching for
food, reproducing, and dying. Both techniques rely on information sharing between particles to
allow the whole population to converge on an optimal configuration. These algorithms have
various applications, including orbital trajectory optimizations. This thesis applies the algorithms
to solve for the optimal impulsive transfer between two circular orbits, which is known to be the
Hohmann transfer. PSO and BFO are then compared to determine which method is less resource-intensive
and can converge on an accurate solution with less error. Future areas of study will
include investigating the comparative effectiveness of these two algorithms in optimizing
different types of trajectory maneuvers.
