2004/11/17 by Tao Zhou, Wen-Jie Bai, Long-Jiu Cheng +2
Computer Science · Mathematics · Physics and Astronomy · #Advanced Optimization Algorithms Research #Cluster (spacecraft) #Computer science #Constraint Satisfaction and Optimization #Continuous optimization #Extension (predicate logic) #Extremal optimization #Global optimization #Heuristic #Mathematical optimization #Mathematics #Meta-optimization #Metaheuristic #Metaheuristic Optimization Algorithms Research #Multi-swarm optimization #Optimization problem #Simulated annealing #Stochastic optimization #cond-mat.mtrl-sci #cond-mat.stat-mech
paper · pdf · doi:10.1103/physreve.72.016702
published as Physical Review E 72, 016702(2005) · 5 pages and 3 figures
arxiv created 2004/11/17 · openalex publication_date 2005/07/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.