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.

翻译：神经网络算法和遗传算法等受生物启发的算法在学术和工程学会都受到大量关注,在本文件中,根据对一种木虱物种,即Porcellio scaber两种主要生存规则的观察,我们提出了一个称为porcellio scaber算法的算法,用于解决一般的不受限制的优化问题,包括差异和非差异的优化问题,以及当地opima的情况。