Multicast routing in datagram internetworks and extended LANs
ACM Transactions on Computer Systems (TOCS)
Steiner's problem in graphs: heuristic methods
Discrete Applied Mathematics - Special issue: combinatorial methods in VLSI
The Steiner problem in distributed computing systems
Information Sciences: an International Journal
Performance optimization of VLSI interconnect layout
Integration, the VLSI Journal
The multimedia multicasting problem
Multimedia Systems
A rearrangeable algorithm for the construction delay-constrained dynamic multicast trees
IEEE/ACM Transactions on Networking (TON)
Ant algorithms for discrete optimization
Artificial Life
A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization
Journal of Heuristics
Multicast Issues for Collaborative Virtual Environments
IEEE Computer Graphics and Applications
A Hybrid GRASP with Perturbations for the Steiner Problem in Graphs
INFORMS Journal on Computing
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
A survey of combinatorial optimization problems in multicast routing
Computers and Operations Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A survey of data multicast techniques, architectures, and algorithms
IEEE Communications Magazine
Destination-driven routing for low-cost multicast
IEEE Journal on Selected Areas in Communications
A distributed architecture for multiplayer interactive applications on the Internet
IEEE Network: The Magazine of Global Internetworking
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The Steiner Tree Problem (STP) in graphs is a well-known NP-hard problem. It has regained attention due to the introduction of new telecommunication technologies, since it is the mathematical structure behind multi-cast communications. The goal of this paper is to design an ant algorithm (called ANT-STP) for the STP in graphs which is better than TM, which is a greedy constructive method for the STP proposed in [34]. We derive ANT-STP from TM as follows: each ant is a constructive heuristic close to TM, but the population of ants can collaborate by exchanging information by the use of the trail systems. In addition, the decision rule used by each individual ant is different from the decision rule used in TM. We compare TM and ANT-STP on a set of benchmark problems of the OR-Library.