in this study it has been tried, to compare results and convergence rate of sensitivity analysis and conjugate gradient algorithms to reduce fuel consumption and increasing engine performance by optimizing the timing of opening and closing valves in xu7/l3 engine. in this study, considering the strength and accuracy of simulation gt-power software in researches on the internal combustion engine...

This paper derives a new algorithm that performs independent component analysis (ICA) by optimizing the contrast function of the RADICAL algorithm. The core idea of the proposed optimization method is to combine the global search of a good initial condition with a gradient-descent algorithm. This new ICA algorithm performs faster than the RADICAL algorithm (based on Jacobi rotations) while stil...

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized ...

Data-based control design methods most often consist of iterative adjustment of the controller’s parameters towards the parameter values which minimize an H2 performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization no process model is used. A limiting factor in the application of these methods is the lack ...

The non-convex behavior presented by nonlinear systems limits the application of classical optimization techniques to solve optimal control problems for these kinds of systems. This paper proposes a hybrid algorithm, namely BA-SD, by combining Bee algorithm (BA) with steepest descent (SD) method for numerically solving nonlinear optimal control (NOC) problems. The proposed algorithm includes th...

In this paper, we propose a new approach for optimizing large-scale non-convex differentiable function subject to linear equality constraints. The proposed method, RPCGB (random perturbation of the conditional gradient method with bisection algorithm), computes search direction by gradient, and an optimal line is found algorithm, which results in decrease cost function. designed guarantee globa...

Global convergence of the long-step aane scaling algorithm for degenerate linear programming problems, 9 tion time can be achieved by using a preconditioned conjugate gradient linear system solver. Convergence of the large step primal aane-scaling algorithm for primal non-degenerate linear programs,

In this paper, we consider the problem of computing the smallest enclosing ball (SEB) of a set of m balls in R, where the product mn is large. We first approximate the non-differentiable SEB problem by its log-exponential aggregation function and then propose a computationally efficient inexact Newton-CG algorithm for the smoothing approximation problem by exploiting its special (approximate) s...

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized ...

A new conjugate gradient method is proposed for applying Powell's symmetrical technique to conjugate gradient methods in this paper, which satisfies the sufficient descent property for any line search. Using Wolfe line searches, the global convergence of the method is derived from the spectral analysis of the conjugate gradient iteration matrix and Zoutendijk's condition. Based on this, two con...