  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 in recent years, 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
MR-EMO based on Evolutionary Multi-objective Optimization, 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 NSGA-II, MOEA/D as well as the theoretically grounded GSEMO.
To further improve the performance of MR-EMO, we propose a specific MOEA,
called GSEMO-SR, using matrix-swap mutation and repair mechanism, 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.
