Continuous extremal optimization for Lennard-Jones Clusters
Tao Zhou, Wen-Jie Bai, Long-Jiu Cheng, and Bing-Hong Wang
TL;DR
This paper introduces continuous extremal optimization (CEO), a heuristic algorithm for continuous optimization, demonstrating its effectiveness on Lennard-Jones clusters by combining global and local search components.
Contribution
The paper presents CEO, a new heuristic extending extremal optimization to continuous problems, with competitive performance using only one adjustable parameter.
Findings
CEO performs competitively with more complex methods
Effective on Lennard-Jones clusters optimization
Uses a simple parameter for tuning
Abstract
In this paper, we explore a general-purpose heuristic algorithm for finding high-quality solutions to continuous optimization problems. The method, called continuous extremal optimization(CEO), can be considered as an extension of extremal optimization(EO) and is consisted of two components, one is with responsibility for global searching and the other is with responsibility for local searching. With only one adjustable parameter, the CEO's performance proves competitive with more elaborate stochastic optimization procedures. We demonstrate it on a well known continuous optimization problem: the Lennerd-Jones clusters optimization problem.
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