Comparative Study of Performance of Chemical Reaction Optimization with Genetic Algorithm (GA)
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10.5120/18768-0069 |
Shekhar L.pandharipande and Aasheesh Kumar Dixit. Article: Comparative Study of Performance of Chemical Reaction Optimization with Genetic Algorithm (GA). International Journal of Computer Applications 107(8):1-8, December 2014. Full text available. BibTeX
@article{key:article,
author = {Shekhar L.pandharipande and Aasheesh Kumar Dixit},
title = {Article: Comparative Study of Performance of Chemical Reaction Optimization with Genetic Algorithm (GA)},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {107},
number = {8},
pages = {1-8},
month = {December},
note = {Full text available}
}
Abstract
Chemical reaction optimisation (CRO) is well suited in searching global solutions to varied nature of optimisation problems. It is amongst newer methods of evolutionary algorithms, nature inspired meta-heuristics for optimisation. A chemical reaction is a process of transforming reactants; the unstable substances into products the relatively stable ones. In chemical reactions, the reactants with some initial energy interact with each other through a sequence of elementary steps. At the end, molecules with minimum energy to support their stable structure are formed. This phenomenon is the source of inspiration in development of algorithm for CRO to get optimal solution. Present work aims at development of CRO using MATLAB©. It also aims in study of dynamics of various parameters of CRO in searching optimum solutions. It is further extended in comparative studies of performance of CRO with other conventional as well as evolutionary optimisation methods such as Genetic Algorithm. Numerical experiments for two test functions in the category of non-linear constrained optimisation problems reported in the literature are carried. The results are indicative of the utility of CRO and its performance is observed at par with other optimisation methods. It can be concluded that there is lot of potential in CRO as an effective alternate optimisation method with universal applicability. There is need for more numerical experimentation to substantiate this claim.
References
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