In this paper, an intelligent-gradient based algorithm is proposed to solve time optimal bang-bang control problem. The proposed algorithm is a combination of an intelligent algorithm called improved particle swarm optimization algorithm (IPSO) in the first stage of optimization process together with a gradient-based algorithm called successive quadratic programming method (SQP) in the second s...

Most local convergence analyses of the sequential quadratic programming (SQP) algorithm for nonlinear programming make strong assumptions about the solution, namely, that the active constraint gradients are linearly independent and that there are no weakly active constraints. In this paper, we establish a framework for variants of SQP that retain the characteristic superlinear convergence rate ...

The field of constrained nonlinear programming (NLP) has been principally challenging to various gradient based optimization techniques. The Sequential quadratic programming algorithm (SQP) that uses active set strategy in solving quadratic programming (QP) subproblems proves to be efficient in locating the points of local optima. However, its efficient determination of the optimal active set h...

A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO–SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global optimum. On the contrary, the ability o...

This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is used to refine the results of GA, further improving the solution quality. The problem formulation is done in the framework named RUNE (fRamework for aUtomated aNalog dEsign), which targets solving no...

Two algorithms that are distinct from the closed algorithm proposed to create inverse kinematics model of UR10 robot: Sequential Quadratic Programming (SQP) and Back Propagation-Sequential (BP-SQP) algorithm. The SQP is an iterative in which fundamental tenet joint’s total rotation radian should be at a minimum when industrial robot reaches target attitude. With this tenet, establishes robot. S...

An algorithm for smooth nonlinear constrained optimization problems is described, in which a sequence of feasible iterates is generated by solving a trust-region sequential quadratic programming (SQP) subproblem at each iteration, and perturbing the resulting step to retain feasibility of each iterate. By retaining feasibility, the algorithm avoids several complications of other trust-region SQ...

the major problem of wind turbines is the great variability of wind power production. the dynamic change of the wind speed returns the quantity of the power injected to networks. therefore, wind–thermal generation scheduling problem plays a key role to implement clean power producers in a competitive environment. in deregulated power systems, the scheduling problem has various objectives than i...

A parallel, filter-based, sequential quadratic programming (SQP) algorithm is implemented and tested for typical general-purpose engineering applications. Constrained engineering test problems, including a finite element simulation, with up to 512 design variables are considered. The accuracy and serial performance of the filter-based algorithm are compared against that of a standard SQP algori...