Siddique Nazmul, Adeli Hojjat
School of Computing and Intelligent Systems, University of Ulster, Northland Road, Londonderry, BT48 7JL UK.
College of Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210 USA.
Cognit Comput. 2017;9(4):411-422. doi: 10.1007/s12559-017-9485-1. Epub 2017 Jun 17.
Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems. This article starts with an overview of the chemical reactions and how it is applied to the optimisation problem. A review of CRO and its variants is presented in the paper. Guidelines from the literature on the effective choice of CRO parameters for solution of optimisation problems are summarised.
在过去三十年中，受自然启发的元启发式算法在机器学习和认知计算范式领域主导了科学文献。化学反应优化（CRO）是一种基于化学反应原理的基于种群的元启发式算法。化学反应被视为一个通过一系列反应将反应物（或分子）转化为产物的过程。在CRO算法中实现这种转化过程以解决优化问题。本文首先概述化学反应及其如何应用于优化问题。文中还对CRO及其变体进行了综述。总结了文献中关于有效选择CRO参数以解决优化问题的指导原则。
