Continuous extremal optimization for Lennard-Jones clusters

Tao Zhou, Wen-Jie Bai, Long-Jiu Cheng, and Bing-Hong Wang
Phys. Rev. E 72, 016702 – Published 6 July 2005

Abstract

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 and consists of two components, one which is responsible for global searching and the other which is responsible for local searching. The CEO’s performance proves competitive with some more elaborate stochastic optimization procedures such as simulated annealing, genetic algorithms, and so on. We demonstrate it on a well-known continuous optimization problem: the Lennard-Jones cluster optimization problem.

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  • Received 17 November 2004

DOI:https://doi.org/10.1103/PhysRevE.72.016702

©2005 American Physical Society

Authors & Affiliations

Tao Zhou1, Wen-Jie Bai2, Long-Jiu Cheng2, and Bing-Hong Wang1,*

  • 1Nonlinear Science Center and Department of Modern Physics, University of Science and Technology of China, Hefei Anhui, 230026, China
  • 2Department of Chemistry, University of Science and Technology of China, Hefei Anhui, 230026, China

  • *Electronic address: bhwang@ustc.edu.cn

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Vol. 72, Iss. 1 — July 2005

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