Nature-Inspired Chemical Reaction Optimisation Algorithms

Nature-Inspired Chemical Reaction Optimisation Algorithms 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cognitive Computation Springer Journals

Nature-Inspired Chemical Reaction Optimisation Algorithms

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Biomedicine; Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics
ISSN
1866-9956
eISSN
1866-9964
D.O.I.
10.1007/s12559-017-9485-1
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Cognitive ComputationSpringer Journals

Published: Jun 17, 2017

References

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