The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

In this project, an area in the small town Tungelsta south of Stockholm, is in focus. Tungelsta is placed in the municipality of Haninge and it is for the planning department on the municipality, this project is made. The task is to compose an arrengement of buildings and open spaces for a 50 hektar area. Today the area is consists of agriculture fields and forests. The area is placed near the railway station, in the city centre of Tungelsta, which makes it interesting and important to work with. There are different opinions about the future plans for the area.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.