Migrant Resettlement by Evolutionary Multi-objective Optimization

Dan-Xuan Liu1; Yu-Ran Gu1; Chao Qian1; Xin Mu2; Ke Tang3

2024

10.1109/TAI.2024.3443790

IEEE Transactions on Artificial Intelligence   影响因子和分区

2691-4581

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this paper, we propose a new framework called Migrant Resettlement by Evolutionary Multi-objective Optimization (MR-EMO), which reformulates migrant resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem. We implement MR-EMO using three MOEAs: the popular Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) as well as the theoretically grounded Global Simple Evolutionary Multi-objective Optimizer (GSEMO). To further improve the performance of MR-EMO, we propose a specific MOEA, called Global Simple Evolutionary Multi-objective Optimizer using matrix-Swap mutation and Repair mechanism (GSEMO-SR), which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO.

EI

其他

1.National Key Laboratory for Novel Software Technology and School of Artificial Intelligence, Nanjing University, Nanjing, China
2.Peng Cheng Laboratory, Shenzhen, China
3.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Dan-Xuan Liu,Yu-Ran Gu,Chao Qian,et al. Migrant Resettlement by Evolutionary Multi-objective Optimization[J]. IEEE Transactions on Artificial Intelligence,2024,PP(99).

Dan-Xuan Liu,Yu-Ran Gu,Chao Qian,Xin Mu,&Ke Tang.(2024).Migrant Resettlement by Evolutionary Multi-objective Optimization.IEEE Transactions on Artificial Intelligence,PP(99).

Dan-Xuan Liu,et al."Migrant Resettlement by Evolutionary Multi-objective Optimization".IEEE Transactions on Artificial Intelligence PP.99(2024).