The multidimensional assignment problem (MAP) is a combinatorial optimization problem that is known to be NP-hard, and therefore, heuristic methods are generally used to find good solutions to it. The problem has many recognized applications such as multi-agent path planning, data association, and multi-searcher problems. Simulated annealing has proven to be effective in solving many combinatorial optimization problems, but we find no references in the literature in which simulated annealing is applied to the MAP. In this chapter, we evaluate a simulated annealing algorithm for solving the MAP and report experimental results using several controlling factors in the algorithm. These factors include the cooling schedule and initial temperature, the neighborhood definition, and the method of finding a starting solution. A design of experiments approach is used to find the most effective controlling factors in the algorithm. Algorithm performance measures include time to solution and quality of solution. For a small problem, the algorithm finds the optimal solution in every case tested. For a large problem, the algorithm finds results that average 1.2 units from the optimal solution. The results show that simulated annealing is an effective method for solving the MAP.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.