Evolutionary Algorithms and Simulated Annealing in the Topological Configuration of the Spanning Tree

In this paper, the author proposes the application of a genetic algorithm and simulated annealing to solve the network planning problem. Compared with other optimisation methods, genetic algorithm and simulated annealing are suitable for traversing large search spaces since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. Genetic algorithm and simulated annealing give an excellent trade-off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. Simulated annealing is a search process that has its origin in the fields of materials science and physics. Simulated annealing, alternatively attempts to avoid becoming trapped in a local optimum. The problem of minimum-cost expansion of network is formulated as a genetic algorithm and simulated annealing. Optimal solution in linear programming is spanning tree. But GA and SA solutions show those are both spanning tree and no spanning tree. key-words: Genetic Algorithm, Simulated Annealing, Evolutionary Algorithms, Spanning Tree, Linear Programming, Network.