@inproceedings{6c56fbda6d7e4b848b108bd49c9eff45,
title = "Adaptive Chemical Reaction Optimization for global numerical optimization",
abstract = "A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.",
keywords = "Chemical Reaction Optimization, adaptive scheme, continuous optimization, evolutionary algorithm, metaheuristic",
author = "Yu, \{James J.Q.\} and Lam, \{Albert Y.S.\} and Li, \{Victor O.K.\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",
year = "2015",
month = sep,
day = "10",
doi = "10.1109/CEC.2015.7257288",
language = "英语",
series = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3192--3199",
booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",
address = "美国",
}