Iterated Greedy Algorithms for the Hop-Constrained Steiner Tree Problem

The Hop-Constrained Steiner Tree problem (HCST) is challenging NP-hard problem arising in the design of centralized telecommunication networks where the reliability constraints matter. In this paper three iterative greedy algorithms are described to find efficient optimized solution to solve HCST on both sparse and dense graphs. In the third algorithm, we adopt the idea of Kruskal algorithm for the HCST problem to reach a better solution. This is the first time such algorithm is utilized in a problem with hop-constrained condition. Computational results on a number of problem instances are derived from well-known benchmark instances of Steiner problem in graphs. We compare three algorithms with a previously known method (Voss's algorithm) in term of effectiveness, and show that the cost of the third proposed method has been noticeably improved significantly, 34.60% in hop 10 on dense graphs and 3.34% in hop 3 on sparse graphs

Greedy Harmony Search Algorithm for the Hop Constrained Connected Facility Location

We present a simple, robust and efficient harmony search algorithm for the Hop Constrained Connected Facility Location problem (HCConFL). The HCConFL problem is NP-hard that models the design of data-management and telecommunication networks in a manner of reliability. In this paper, we customize harmony search algorithm to solve the HCConFL problem. To arrive to quick, optimal cost of each solution, we use a new greedy approach expanding idea of Kruskal algorithm in our objective function. We also use a new greedy method combined with harmony search to obtain a good approximation in an efficient computational time. The algorithm was evaluated on the standard OR Library benchmarks. Computational results show that with high frequencies the modified harmony search algorithm produces optimal solutions to all benchmarks very quickly. We also solve the problem with another heuristic algorithm including the variable neighborhood search, the tabu search, to evaluate our algorithm