A Coevolutionary Estimation of Distribution Algorithm for Group Insurance Portfolio

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 11; pp. 6714 - 6728
• Main Authors: Shi, Wen, Chen, Wei-Neng, Kwong, Sam, Zhang, Jie, Wang, Hua, Gu, Tianlong, Yuan, Huaqiang, Zhang, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational modeling; Computer science; Cooperative coevolution (CC); estimation of distribution algorithm (EDA); Group insurance; Insurance; Insurance industry; Insurance policies; insurance portfolio; Investment; Optimization; Particle swarm optimization; particle swarm optimization (PSO); Portfolios; Resource management
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2021.3096013

With the rapid development of the insurance industry, more diverse insurance products are produced for consumers. Insurance portfolio problems have received increasing attention. While most studies focus on insurance portfolio problem for a single insured, insurance portfolio problems for a specific group of insured are even more intricate but little attention has been paid to. In this article, we propose a group insurance portfolio model for investment allocation of several insurance policies so that the total payout of the whole group can be maximized. The statistical average value of each parameter is considered in the model to approximate the expectation payout of the group insurance portfolio problem. To solve this problem, a coevolutionary estimation of distribution algorithm (EDA) utilizing the divide-and-conquer strategy is proposed. First, as the payout of each insured under a certain portfolio plan can be calculated separately, the proposed approach decomposes the group insurance portfolio problem into several single-insured insurance portfolio problems. In this way, the dimension of the optimization problem becomes lower compared to the original problem. An adaptive EDA is proposed to optimize the portfolio plan of each insured independently. Second, the group insurance portfolio problem remains a nonseparable problem since the investment amount of each insured is limited by the total investable amount of the whole group. A particle swarm optimization algorithm is adopted to cooperate with the EDA to optimize the proportion of allocation to each insured. The proposed algorithm is verified on various scenarios. The experimental results validate that the proposed approach is effective for the group insurance portfolio problem.