Research Article
Optimisation of asset allocation and reinsurance strategy by the mean of a genetic algorithm : NSGA-II
@INPROCEEDINGS{10.4108/eai.24-4-2019.2284089, author={Mehdi Echchelh}, title={Optimisation of asset allocation and reinsurance strategy by the mean of a genetic algorithm : NSGA-II}, proceedings={Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 2019, Faculty of Sciences, Ibn Tofa\~{n}l University -K\^{e}nitra- Morocco}, publisher={EAI}, proceedings_a={ICCWCS}, year={2019}, month={5}, keywords={multi-objective optimization genetic algorithm nsga-ii insurance reinsurance asset allocation}, doi={10.4108/eai.24-4-2019.2284089} }- Mehdi Echchelh
Year: 2019
Optimisation of asset allocation and reinsurance strategy by the mean of a genetic algorithm : NSGA-II
ICCWCS
EAI
DOI: 10.4108/eai.24-4-2019.2284089
Abstract
The management of insurance companies is a complex problem in which several indicators intervene and for which the interactions between these indicators and the management levers (reinsurance strategy, asset allocation, etc.) are not simple. We apply one of the most popular multi-objective optimization methods, namely NSGA-II. The implemented algorithm allows to find almost 98% of the hypervolume of the Pareto front in 20 iterations. In addition, the quality of representation of the Pareto front seems to be independent of the number of business of lines to be optimized, which may suggest that the work in this study may be scaled to larger companies for business use.
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