  Extreme learning machine (ELM) as a simple and rapid neural network has been
shown its good performance in various areas. Different from the general single
hidden layer feedforward neural network (SLFN), the input weights and biases in
hidden layer of ELM are generated randomly, so that it only takes a little
computation overhead to train the model. However, the strategy of selecting
input weights and biases at random may result in ill-posed problem. Aiming to
optimize the conditioning of ELM, we propose an effective particle swarm
heuristic algorithm called Multitask Beetle Antennae Swarm Algorithm (MBAS),
which is inspired by the structures of artificial bee colony (ABS) algorithm
and Beetle Antennae Search (BAS) algorithm. Then, the proposed MBAS is applied
to optimize the input weights and biases of ELM. Experiment results show that
the proposed method is capable of simultaneously reducing the condition number
and regression error, and achieving good generalization performances.
