Orthogonal Frequency Division Multiple Access (OFDMA) is the promising access technique for future networks which allows multiple users to transmit simultaneously. The problem of allocating resources (subcarriers, bit and power) among the communicating users in multiuser orthogonal frequency division multiplexing (OFDM) system is a constraint optimization problem. Bio-inspired Networking represents an emerging area to obtain optimal solutions for handling various challenges in the networking scenario. Nature is our mother and an inspiration from nature always gives the best. Bio-inspired algorithms are known for their efficiency in solving NP hard problems. This paper focusses on realizing the rate adaptive resource allocation problem in OFDM systems using bio-inspired approach. The resource allocation problem using Genetic Algorithm, algorithms based on foraging behavior in ants and flocking behavior in birds have already been modelled. The paper suggests the application of Bees Algorithm for resource allocation in OFDMA with the goal of maximizing the data rate of each user.
The design of polymer insulator depends on the surface electric field gradient. Increased electric field distribution generates corona discharge and leads to breakdown. Corona discharge has become an important factor not only because of corona losses but also due to electromagnetic interferences. Hence proper design optimization is necessary while designing the corona ring and grading ring which is employed at the critical region of the insulator to reduce the corona discharge. In this paper, optimized ring designs are identified for a 400kV tension type insulator. Three phase tension insulator along with the tower model is selected and its electric field distribution is calculated using finite element analysis. A 3D model design is developed using FEM based software. Seven structure parameters of the corona and grading rings are varied and the electric field is calculated. The optimized ring design is then achieved using multi-objective genetic algorithm. The obtained corona ring and grading ring parameters are compared with the simulated values and the error is less than 3%. Hence the proposed methodology can be used for prediction of corona ring and grading ring design, as the electric field is below the critical value.
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