  This paper presents a self-optimizing solution for Mobility Load Balancing
(MLB). The MLB-SON is performed in two phases. In the first, a MLB controller
is designed using Multi-Objective Particle Swarm Optimization (MO-PSO) which
incorporates a priori expert knowledge to considerably reduce the search space
and optimization time. The dynamicity of the optimization phase is addressed.
In the second phase, the controller is pushed into the base stations to
implement the MLB SON. The method is applied to dynamically adapt Handover
Margin parameters of a large scale LTE network in order to balance traffic of
the network eNodeBs. Numerical results illustrate the benefits of the proposed
solution.
