Lifetimes of migration

Agent Based Modeling (ABM) is a bottom-up approach that has been used to study adaptive group (collective) behavior. ABM is an analogical system that aids ethologists in constructing novel hypotheses, and allows the investigation of emergent phenomena in experiments that could not be conducted in nature [15], [2], [12], [11]. Many studies in ethology have formalized mathematical models of collective migration behavior [1], but few have examined the impact of phenotypic traits (such as lifetime length) on the learning and evolution of collective migration behavior [9], [4]. The first objective of this research is to test the impact of agent lifetime length on the adaptation of collective migration behaviors in a virtual environment. Agent behavior is adapted with a hybrid Particle Swarm Optimization (PSO) method that integrates learning and evolution. Learning (lifetime learning) refers to a process whereby agents learn new behaviors during their lifetime [13], [3]. Evolution (genetic learning) refers to behavioral adaptation over successive lifetimes (generations) of an agent population [5]. The second objective is to demonstrate these hybrid PSO methods are appropriate for modeling the adaptation of collective migration behaviors in an ABM. The motivation is that PSO methods combined with evolution and learning approaches have received little attention as ABMs for potentially addressing (supporting or refuting) hypotheses posited in ethological literature. The task was for an agent group (flock) to locate a migration point during a simulated season in a virtual environment, where a season consisted of X simulation iterations.