Abstract Reinsurance is a common practice in financial engineering that aims to hedge risk and reduce the probability of bankruptcy by obtaining essential services from more prominent insurance companies. Hence, insurance companies might issue contracts in the market to transfer risk, especially against natural disasters. To perform the risk hedge, it is crucial to offer the market the best contract option, i.e., an optimized reinsurance contract, which is a combinatorial problem with a complex search space. In this context, metaheuristics have proven to be practical algorithms for discovering solutions to complex search spaces in a feasible time. Thus, this paper proposes a modified bio-inspired metaheuristic, the Enhanced Multiobjective Particle Swarm Optimization (E-MOPSO), for reinsurance contract optimization, allowing insurers to hedge the maximum possible amount of risk through reinsurance and protect insurance companies against massive claims resulting from natural catastrophes. To verify the effectiveness of those multiobjective solutions, the algorithm was implemented in R and compared against state-of-the-art algorithms such as MOPBIL and VEPBIL, using ANOVA and the Tukey test in a real-world contract comprising 7 layers. Moreover, a 15-layer semi-synthetic dataset was created using the original one to test the algorithm’s ability. Results show that the E-MOPSO approach overcomes the referred algorithms regarding computing time and hypervolume.
Cortes et al. (Wed,) studied this question.