Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms

Risks

Sancho Salcedo-Sanz¹  Leo Carro-Calvo¹  Mercè Claramunt²  Ana Castañer² 

[1] Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares 28805, Madrid, Spain; E-Mail:;Dept. Matemàtica Econòmica, Financera i Actuarial, Universitat de Barcelona, Barcelona 08034, Spain; E-Mails:

关键词: reinsurance;    optimization problems;    evolutionary-based algorithms;   

DOI  :  10.3390/risks2020132

来源: mdpi

【 摘 要 】

This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems (problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program)), which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques (evolutionary programming and particle swarm optimization). We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.