  Bio-inspired algorithms such as neural network algorithms and genetic
algorithms have received a significant amount of attention in both academic and
engineering societies. In this paper, based on the observation of two major
survival rules of a species of woodlice, i.e., porcellio scaber, we present an
algorithm called the porcellio scaber algorithm (PSA) for solving general
unconstrained optimization problems, including differentiable and
non-differential ones as well as the case with local optima. Numerical results
based on benchmark problems are presented to validate the efficacy of PSA.
