Abstract: In this study, we study the application of a kind of nonmonotone line search in BFGS algorithm for solving unconstrained optimization problems. This nonmonotone line search is belongs to Armijo-type line searches and when the step size is being computed at each iteration, the initial test step size can be adjusted according to the characteristics of objective functions. The global con...

We propose railway line planning optimization models that determines the frequency of each train so that it can meet passenger origindestination demands while minimizing the related costs. Most line planning models assume that all trains on a supply network stop at all intermediate stations. However to minimize passenger travelling time and provide train service to as many stations as possible,...

We discuss an optimization model for the line planning problem in public transport in order to minimize operation costs while guaranteeing a certain level of quality of service, in terms of available transport capacity. We analyze the computational complexity of this problem for tree network topologies as well as several categories of line operations that are important for the Quito Trolebús sy...

Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging task for the academic research currently is to address large-scale, complex optimization problems under various uncertainties. Therefore, investigations on the development of stochastic optimization approaches are necessi...

This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system. The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control input. The results confirm the ef...