sequential quadratic programming (SQP) methods are commonly used to solve constrained non-linear optimisation problems. However, in recent years there has been great improvement in using evolutionary algorithms to sol...
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sequential quadratic programming (SQP) methods are commonly used to solve constrained non-linear optimisation problems. However, in recent years there has been great improvement in using evolutionary algorithms to solve non-linear optimisations problems. The difficulty has been determining a correct method for implementing evolutionary algorithm for a non-linear optimisation problem with constraints. In this paper, we are combining the strengths of the traditional SQP method with an evolutionary algorithm, particle swarm optimisation (PSO) for solving a constrained non-linear optimisation problem with equality and inequality constraints. We propose a constrained PSO algorithm be used to solve the quadraticprogramming (QP) subproblem within the SQP method.
The sequential quadratic programming method developed by Wilson, Han and Powell may fail if the quadraticprogramming subproblems become infeasible, or if the associated sequence of search directions is unbounded. Thi...
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The sequential quadratic programming method developed by Wilson, Han and Powell may fail if the quadraticprogramming subproblems become infeasible, or if the associated sequence of search directions is unbounded. This paper considers techniques which circumvent these difficulties by modifying the structure of the constraint region in the quadraticprogramming subproblems. Furthermore, questions concerning the occurrence of an unbounded sequence of multipliers and problem feasibility are also addressed.
The determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is ...
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The determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for the optimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizing the production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of this algorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergence characteristics and robustness of the proposed method have been explored through comparisons with results reported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequential quadratic programming is effective compared to other techniques carried out by different researchers.
In this paper, we modelled a multi-product four-level integrated supply chain network problem consisting of a supplier, a producer, a wholesaler, and multiple retailers. The aims of this paper are both to find the num...
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In this paper, we modelled a multi-product four-level integrated supply chain network problem consisting of a supplier, a producer, a wholesaler, and multiple retailers. The aims of this paper are both to find the number of optimum stockpiles in each level and the optimum period length such that the total cost of the chain is minimized while constraints such as limited procurement cost, limited space, and limited ordering cost are satisfied. Mentioned problem is a large nonlinear programming problem so a developed sequential quadratic programming (SQP) is used to solve the problem. Next, three numerical examples are solved in order to demonstrate the applicability of the proposed methodology and also to evaluate the performance of SQP. At the end, a sensitivity analysis is performed on the change rate of the integrated objective function obtained based on the change rate of the period length.
We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced i...
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We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced is motivated by the necessity to deviate from the standard approach when solving large problems. Specifically we no longer require a minimizer of the QP subproblem to be determined or particular Lagrange multiplier estimates to be used. Our main focus is on an SQP algorithm that uses a particular augmented Lagrangian merit function. New results are derived for this algorithm under weaker conditions than previously assumed;in particular, it is not assumed that the iterates lie on a compact set.
This paper presents a distributed optimal power flow approach based on Augmented Lagrangian (AL) and sequential quadratic programming (SQP). It is able to separate the OPF into smaller sub-problems, which could be ite...
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ISBN:
(纸本)9781467368537
This paper presents a distributed optimal power flow approach based on Augmented Lagrangian (AL) and sequential quadratic programming (SQP). It is able to separate the OPF into smaller sub-problems, which could be iteratively solved individually using the SQP. This utilizes the SQP for largescale problems with non-linear objective functions and constraints. Simulation and comparison using the IEEE 30 and 118 buses examples show that the proposed distributed approach is able to achieve comparable performance with other benchmark centralized solvers provided by the FMINCON in MATPOWER. This suggests the proposed approach may serve an attractive alternative to other OPF algorithms.
In order to efficiently construct anticlastic concrete shell structures in architecture, flexible formwork can be used, whose main component is a cable net under tension. To cope with the fabrication tolerances of the...
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ISBN:
(纸本)9781467386838
In order to efficiently construct anticlastic concrete shell structures in architecture, flexible formwork can be used, whose main component is a cable net under tension. To cope with the fabrication tolerances of the cable net and thus to reduce the deviations between the nominal digital design model and the as-built one, which is needed to guarantee the properties of the shell, we propose a control algorithm which iteratively steers the geometry of the cable net to the desired one. Our contribution in this paper is twofold. We formulate an optimal control problem and provide two different formulations of the nonlinear equality and inequality constraints. Whereas one of the formulations is a set of implicit nonlinear equations, the other one is the solution to a second-order cone program, which can efficiently be solved. We use both formulations and combine them into a control algorithm, which is based on sequential quadratic programming (SQP), and where the solution in each iteration is feasible for the nonlinear constraints. A simulation example is presented to demonstrate the performance of the developed control algorithm.
We consider the problem of refining an abstract task plan into a motion trajectory. Task and motion planning is a hard problem that is essential to long-horizon mobile manipulation. Many approaches divide the problem ...
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ISBN:
(纸本)9781509037636
We consider the problem of refining an abstract task plan into a motion trajectory. Task and motion planning is a hard problem that is essential to long-horizon mobile manipulation. Many approaches divide the problem into two steps: a search for a task plan and task plan refinement to find a feasible trajectory. We apply sequential quadratic programming to jointly optimize over the parameters in a task plan (e.g., trajectories, grasps, put down locations). We provide two modifications that make our formulation more suitable to task and motion planning. We show how to use movement primitives to reuse previous solutions (and so save optimization effort) without trapping the algorithm in a poor basin of attraction. We also derive an early convergence criterion that lets us quickly detect unsatisfiable constraints so we can re-initialize their variables. We present experiments in a navigation amongst movable objects domain and show substantial improvement in cost over a backtracking refinement algorithm.
This paper proposes a method of optimizing the wavelength sweep in the spectral matching imager (SMI) by sequential quadratic programming (SQP). The proposed method determines a weight function that appears in the spe...
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ISBN:
(纸本)9784907764432
This paper proposes a method of optimizing the wavelength sweep in the spectral matching imager (SMI) by sequential quadratic programming (SQP). The proposed method determines a weight function that appears in the spectral correlation integral by minimizing a cost functional composed of the squared correlation coefficient between two reference spectra, while properly incorporating equality and inequality constraints. A norm term is newly added to the cost functinoal, which increases the reliability of the solution. Experimental results confirm that the obtained wavelength sweep function, which is derived from the optimized weight function and tends to be nonlinear to time, enables the SMI to distinguish two objects with very similar spectra more easily than in the case of time-linear wavelength sweep.
This paper investigates the optimal operation problem for distribution networks with the integration of distributed generation (DG). By considering the objectives of minimal line loss, minimal voltage deviation and ma...
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ISBN:
(纸本)9781509054183
This paper investigates the optimal operation problem for distribution networks with the integration of distributed generation (DG). By considering the objectives of minimal line loss, minimal voltage deviation and maximum DG active power output, the proposed operational optimization formulation is a multi-object optimization problem. Through normalization of each objective function, the multi-objective optimization is transformed to single objective optimization. To solve such a non-convex problem, the trust region robust sequential quadratic programming (TRR-SQP) method is proposed which iteratively approximates by a quadraticprogramming with the trust region guidance. Numerical tests on IEEE 33-bus, and multiple actual systems show the applicability, and comparisons with the primal-dual interior point method and sequential linear programming method are provided.
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