Optimization of N+1 queens problem using neral networks and the proximity rule of initialiation

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dc.contributor.authorMuhammad Waqas
dc.date.accessioned2012-12-28T10:14:29Z
dc.date.available2012-12-28T10:14:29Z
dc.date.issued2010
dc.description.abstractThe neural networks proved very handy in order to solve combinatorial optimization problems for the last two decades. Especially the Hopfield-Tank neural network model is extensively applied to obtaining an optimal/feasible solution to many different NP combinatory optimization problems like travelling salesman problem (TSP) and NP-hard combinatory optimization problem like N Queens Problem. This thesis describes a neural network optimizer/scheduler that optimizes a solution for a highly complicated version of N Queens Problem, i.e. N+1 non-threatening Queens on a N*N chessboard with an intermediate pawn on it. The behavior of the network is evaluated using asynchronous as well as synchronous mode of updating the neurons. Theoretical soundness of the network is established with simulation. Simulations show that the proposed neural network is capable of finding the optimized solution and is convergent to the global minima in 90% of the trials with polynomial average computational complexity.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/672
dc.language.isoenen_US
dc.publisherUniversity of Management and Technologyen_US
dc.subjectMS Computure Scienceen_US
dc.subjectNeural Network Modelen_US
dc.subjectCombinatory Optimizationen_US
dc.titleOptimization of N+1 queens problem using neral networks and the proximity rule of initialiationen_US
dc.titleOptimization of n+1 queens problem using neral networks and the proximity rule of initialiationen_us
dc.typeThesisen_US
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