Two-Stage Eagle Strategy with Differential Evolution

TL;DR

This paper investigates a two-stage optimization strategy called Eagle Strategy, combining different algorithms, specifically differential evolution, to significantly improve search efficiency in solving real-world optimization problems.

Contribution

It introduces and evaluates a two-stage Eagle Strategy that enhances global optimization efficiency by combining algorithms, demonstrated with differential evolution on practical problems.

Findings

Reduced computational effort by up to 10 times

Effective in solving pressure vessel and speed reducer design problems

Demonstrates improved search efficiency in real-world applications

Abstract

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.