Multiprocessor scheduling and neural network training methods using shuffled frog-leaping algorithm

• Development of learning method for ANN.
• Development of a method for optimization of RBFNN.
• Use of SFLA in task scheduling.
• Use of SFLA trained ANN in task scheduling.
• Use of SFLA trained RBFNN in task scheduling.

In this paper, we designed novel methods for Neural Network (NN) and Radial Basis function Neural Networks (RBFNN) training using Shuffled Frog-Leaping Algorithm (SFLA). This paper basically deals with the problem of multi-processor scheduling in a grid environment. We, in this paper, introduce three novel approaches for the task scheduling problem using a recently proposed Shuffled Frog-Leaping Algorithm (SFLA). In a first attempt, the scheduling problem is structured as a problem of optimization and solved by SFLA. Next, this paper makes use of SFLA trained Artificial Neural Network (ANN) and Radial Basis function Neural Networks (RBFNN) for the problem of task scheduling. Interestingly, the proposed methods yield better performance than contemporary algorithms as evidenced by simulation results.