In this paper we present a metaheuristic for global optimization called General Algorithmic Search (GAS). Specifically, GAS is a stochastic, single-objective method that evolves a swarm of agents in search of a global extremum. Numerical simulations with a sample of 31 test functions show that GAS outperforms Basin Hopping, Cuckoo Search, and Differential Evolution, especially in concurrent optimization, i.e., when several runs with different initial settings are executed and the first best wins. Python codes of all algorithms and complementary information are available online.

翻译：在本文中,我们提出了一个全球优化的计量经济学,称为通用算术搜索(GAS ) 。 具体地说, GAS 是一种随机、单一目标的方法,在寻找全球极限时使各种物剂形成群状。 带有31个测试功能样本的数值模拟显示, GAS 优于盆地翻滚、库库搜索和差异进化,特别是在同时优化的情况下,即当执行几个带有不同初始设置的运行和首个最佳赢家时。 所有算法的Python代码和补充信息都可以在线获得。