A Simulated Annealing approach for solving Minimum Manhattan Network Problem

In this paper we address the Minimum Manhattan Network (MMN) problem. It is an important geometric problem with vast applications. As it is an NP-complete discrete combinatorial optimization problem we employ a simple metaheuristic namely Simulated Annealing. We have also developed benchmark datasets and tested our algorithm with the dataset.

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