Enhanced multiobjective population-based incremental learning with applications in risk treaty optimization

The purpose of this paper is to revisit the Multiobjective Population-Based Incremental Learning method and show how its performance can be improved in the context of a real-world financial optimization problem . The proposed enhancements lead to both better performance and improvements in the quality of solutions, which can represent millions of dollars for the insurance company in terms of recoveries. Its performance was assessed in terms of runtime and speedup when parallelized. Also, metrics such as the average number of solutions, the average hypervolume, and coverage have been used in order to compare the Pareto frontiers obtained by both the original and enhanced methods. Results indicated that the proposed method is 22.1% faster, present more solutions in the average (better defining the Pareto frontier) and often generates solutions having larger hypervolumes. The method achieves a speedup of 15.7 on 16 cores of a dual socket Intel multi-core machine when solving a Reinsurance Contract Optimization problem involving 15 layers or sub-contracts .