  Digital twins have shown a great potential in supporting the development of
wireless networks. They are virtual representations of 5G/6G systems enabling
the design of machine learning and optimization-based techniques. Field data
replication is one of the critical aspects of building a simulation-based twin,
where the objective is to calibrate the simulation to match field performance
measurements. Since wireless networks involve a variety of key performance
indicators (KPIs), the replication process becomes a multi-objective
optimization problem in which the purpose is to minimize the error between the
simulated and field data KPIs. Unlike previous works, we focus on designing a
data-driven search method to calibrate the simulator and achieve accurate and
reliable reproduction of field performance. This work proposes a search-based
algorithm based on mixedvariable particle swarm optimization (PSO) to find the
optimal simulation parameters. Furthermore, we extend this solution to account
for potential conflicts between the KPIs using {\alpha}-fairness concept to
adjust the importance attributed to each KPI during the search. Experiments on
field data showcase the effectiveness of our approach to (i) improve the
accuracy of the replication, (ii) enhance the fairness between the different
KPIs, and (iii) guarantee faster convergence compared to other methods.
