Power and resource allocation using modified binary particle swarm optimization in neural network
Kumaresan Anusha, G I Shamini · 2016
The use of resources in wireless sensor network's (WSN) is usually highly related to the execution of task which consume reasonable operating and communication bandwidth. To allocate the workload of each task to proper nodes in an efficient manner a task allocation is needed. But it is a typical problem in the area of high performance computing. But this can be overcome, by applying the power and resource allocation. In this paper the power and resource allocation using modified binary particle swarm optimization in neural networks can be done in order to get the optimized network. In this the power and resource allocation plays an important role in order to overcome the fading channel and the co-channel interference. By using the modified binary particle swarm optimization the convergence speed is largely improved with the transfer function and position updating formula and also the diversity of particles is improved and the problem of local minima is avoided with the mutation operation.