Simulated annealing: theory and applications
Simulated annealing: theory and applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Modelling, simulation and optimisation of network planning methods
ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
An enhanced congestion-driven floorplanner
WSEAS Transactions on Circuits and Systems
Some numerical experiments on multi-criterion tabu programming for finding Pareto-optimal solutions
WSEAS TRANSACTIONS on SYSTEMS
Using simulated annealing algorithm for maximizing social utility in dynamic spectrum management
WSEAS TRANSACTIONS on COMMUNICATIONS
New methods for system planning
Applied Soft Computing
Utilization of modified simulating annealing as a tool for parallel computing
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
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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.