  Edge caching is a new paradigm that has been exploited over the past several
years to reduce the load for the core network and to enhance the content
delivery performance. Many existing caching solutions only consider homogeneous
caching placement due to the immense complexity associated with the
heterogeneous caching models. Unlike these legacy modeling paradigms, this
paper considers heterogeneous content preference of the users with
heterogeneous caching models at the edge nodes. Besides, aiming to maximize the
cache hit ratio (CHR) in a two-tier heterogeneous network, we let the edge
nodes collaborate. However, due to complex combinatorial decision variables,
the formulated problem is hard to solve in the polynomial time. Moreover, there
does not even exist a ready-to-use tool or software to solve the problem. We
propose a modified particle swarm optimization (M-PSO) algorithm that
efficiently solves the complex constraint problem in a reasonable time. Using
numerical analysis and simulation, we validate that the proposed algorithm
significantly enhances the CHR performance when comparing to that of the
existing baseline caching schemes.
