Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications.
@article{arxiv.1203.6586,
title = {Two-Stage Eagle Strategy with Differential Evolution},
author = {Xin-She Yang and Suash Deb},
journal= {arXiv preprint arXiv:1203.6586},
year = {2012}
}