The Steiner tree problem in graphs presents highest difficulties in case of graphs with a medium density of terminals. In fact, most of the efficient algorithms that have been developed to deal with the Steiner tree problem show their worse behaviour for a terminal density between 15 and 30%. Here we present a new bio-inspired approach based on a biological virus infection called viral system (VS) that emulates a massive infection in an organism (representing a massive exploration of the feasibility region). The approach is tested in a large-sized library of problems for which the optimal solution is known, and it is compared to other very efficient soft computing methodologies such as Tabu search and genetic algorithms that have been developed for this specific problem. The VS massive infection algorithm improves the results provided by such approaches.
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