Simulated Annealing : Simulated Annealing for Large Scale Optimization in Wireless Communications
Abstract: In this thesis a simulated annealing algorithm is employed as an optimization tool for a large scale optimization problem in wireless communication. In this application, we have 100 places for transition antennas and 100 places for receivers, and also a channel between each position in both areas. Our aim is to nd, say the best 3 positions there, in a way that the channel capacity is maximized. The number of possible combinations is huge. Hence, nding the best channel will take a very long time using an exhaustive search. To solve this problem, we use a simulated annealing algorithm and estimate the best answer. The simulated annealing algorithm chooses a random element, and then from the local search algorithm, compares the selected element with its neighbourhood. If the selected element is the maximum among its neighbours, it is a local maximum. The strength of the simulated annealing algorithm is its ability to escape from local maximum by using a random mechanism that mimics the Boltzmann statistic.
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