General Algorithmic Search
Optimization and Control
2017-05-25 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1705.08691,
title = {General Algorithmic Search},
author = {Sergio Hernández and Guillem Duran and José M. Amigó},
journal= {arXiv preprint arXiv:1705.08691},
year = {2017}
}
Comments
12 pages, 2 figures