  Along with the development of manufacture and services, the problem of
distribution network optimization has been growing in importance, thus
receiving much attention from the research community. One of the most recently
introduced network optimization problems is the Clustered Shortest-Path Tree
Problem (CluSTP). Since the problem is NP-Hard, recent approaches often prefer
to use approximation algorithms to solve it, several of which used Evolutionary
Algorithms (EAs) and have been proven to be effective. However, most of the
prior studies directly applied EAs to the whole CluSTP problem, which leads to
a great amount of resource consumption, especially when the problem size is
large. To overcome these limitations, this paper suggests a method for reducing
the search space of the EAs applied to CluSTP by decomposing the original
problem into two sub-problems, the solution to one of which is found by an EAs
and that to the other is found by another method. The goal of the first
sub-problem is to determine a spanning tree which connects among the clusters,
while the goal of the second sub-problem is to determine the best spanning tree
for each cluster. In addition, this paper proposes a new EAs, which can be
applied to solve the first sub-problem and suggests using the Dijkstra's
algorithm to solve the second sub-problem. The proposed approach is
comprehensively experimented and compared with existing methods. Experimental
results prove that our method is more efficient and more importantly, it can
obtain results which are close to the optimal results.
