This paper presents an extended propulsion system (EPS) model to solve the EPS fuel economy optimization using the nonlinear programming method with linear constraints obtained from the nonlinear ones and the nonlinea...
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This paper presents an extended propulsion system (EPS) model to solve the EPS fuel economy optimization using the nonlinear programming method with linear constraints obtained from the nonlinear ones and the nonlinear aerothermodynamics model of the EPS. This greatly simplifies the structure of EPS and develops and simplifies the nonlinear performance optimization method. Numerical simulations were conducted for turbofan engines with high thrust-weight ratio and dual rotors, and the optimal steady operating point was obtained for EPS's fuel economy.
A general super-memory gradient projection method was generalized to solve the nonlinear programming problems about the nonlinear equality constraints and in-equality constraints by using general projection matrix. Th...
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A general super-memory gradient projection method was generalized to solve the nonlinear programming problems about the nonlinear equality constraints and in-equality constraints by using general projection matrix. The global convergence properties of the new method were discussed. The new method is of stability in calculation and demands less number of iterations, shorter computing time, strong convergent properties and weaker conditions for convergence than the original algorithm. The numerical results illustrate that the new method is more effective.
Memory gradient methods are used for unconstrained optimization, especially large scale problems. The first idea of memory gradient methods was proposed by Miele and Cantrell (1969) and subsequently extended by Cragg ...
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Memory gradient methods are used for unconstrained optimization, especially large scale problems. The first idea of memory gradient methods was proposed by Miele and Cantrell (1969) and subsequently extended by Cragg and Levy (1969). Recently Narushima and Yabe (2006) proposed a new memory gradient method which generates a descent search direction for the objective function at every iteration and converges globally to the solution if the Wolfe conditions are satisfied within the line search strategy. On the other hand, Sun and Zhang (2001) proposed a particular choice of step size, and they applied it to the conjugate gradient method. In this paper, we apply the choice of the step size proposed by Sun and Zhang to the memory gradient method proposed by Narushima and Yabe and establish its global convergence.
In this paper the solution of nonlinear programming problems by a Sequential Quadratic programming (SQP) trust-region algorithm is considered. The aim of the present work is to promote global convergence without the n...
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In this paper the solution of nonlinear programming problems by a Sequential Quadratic programming (SQP) trust-region algorithm is considered. The aim of the present work is to promote global convergence without the need to use a penalty function. Instead, a new concept of a "filter" is introduced which allows a step to he accepted if it reduces either the objective function or the constraint violation function. Numerical tests on a wide range of test problems are very encouraging and the new algorithm compares favourably with LANCELOT and an implementation of Sl(1) QP.
As an extension of the hybrid Genetic Algorithm-HGA proposed by Tang et al. (Comput. Math. Appl. 36 (1998) 11). this paper focuses on the critical techniques in the application of the GA to nonlinear programming (NLP)...
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As an extension of the hybrid Genetic Algorithm-HGA proposed by Tang et al. (Comput. Math. Appl. 36 (1998) 11). this paper focuses on the critical techniques in the application of the GA to nonlinear programming (NLP) problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible points/chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are proposed to formulate and evaluate the infeasible chromosomes. The extended version of concepts of dominated semi-feasible direction (DSFD), feasibility degree (FD,) of semi-feasible direction, feasibility degree (FD,) of infeasible points 'belonging to' feasible domain are introduced. Combining the new evaluation functions and weighted gradient direction search into the Genetic Algorithm, an extended hybrid Genetic Algorithm (EHGA) is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints. Simulation shows that this new algorithm is efficient.
An efficient algorithm for solving nonlinear programs with noisy equality constraints is introduced and analyzed. The unknown exact constraints are replaced by surrogates based on the bundle idea, a well-known strateg...
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An efficient algorithm for solving nonlinear programs with noisy equality constraints is introduced and analyzed. The unknown exact constraints are replaced by surrogates based on the bundle idea, a well-known strategy from nonsmooth optimization. This concept allows us to perform a fast computation of the surrogates by solving simple quadratic optimization problems, control the memory needed by the algorithm, and prove the differentiability properties of the surrogate functions. The latter aspect allows us to invoke a sequential quadratic programming method. The overall algorithm is of the quasi-Newton type. Besides convergence theorems, qualification results are given and numerical test runs are discussed.
In recent work, the local convergence behavior of path-following interior-point methods and sequential quadratic programming methods for nonlinear programming has been investigated for the case in which the assumption...
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In recent work, the local convergence behavior of path-following interior-point methods and sequential quadratic programming methods for nonlinear programming has been investigated for the case in which the assumption of linear independence of the active constraint gradients at the solution is replaced by the weaker Mangasarian-Fromovitz constraint qualification. In this paper, we describe a stabilization of the primal-dual interior-point approach that ensures rapid local convergence under these conditions without enforcing the usual centrality condition associated with path-following methods. The stabilization takes the form of perturbations to the coefficient matrix in the step equations that vanish as the iterates converge to the solution.
A heat exchanger network design for a particular set of hot and cold stream duties requires multiple stream splits for minimum energy use. A modern NLP solver, FilterSQP, is applied to minimize a total cost function t...
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A heat exchanger network design for a particular set of hot and cold stream duties requires multiple stream splits for minimum energy use. A modern NLP solver, FilterSQP, is applied to minimize a total cost function that takes account of capital and energy costs. The best solution contains fewer exchangers than the initial network. There are multiple local optima at various cost levels. They contain different subsets of exchangers of the initial design, which constitutes a partial superstructure for the problem. Several optimization alternatives are examined: for the model formulation, leading to problems with different optimum costs, for the way the objective is written, which affects the formal degree of nonlinearity in the equations or objective, and for the way the solver operates. For each combination of options, tests were run using widely different initial values for the Trust Region size, a parameter in the solver. FilterSQP solved the network with impressive reliability and efficiency from several starting guesses, some of which were highly arbitrary. From even the most unpromising initial guesses, the solver converged to a local optimum in nearly all cases.
A simple and efficient method for eliminating branch overloads in power systems is presented in this paper. The overloads are eliminated by corrective control actions, which are computed by an efficient and accurate n...
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A simple and efficient method for eliminating branch overloads in power systems is presented in this paper. The overloads are eliminated by corrective control actions, which are computed by an efficient and accurate nonlinear programming method. Generation rescheduling and load shedding are the main controls used. The idea of adaptative local optimization is introduced, making the computation of appropriate generation rescheduling a very efficient process, Load shedding is used as a last resort, when further generation rescheduling is no longer possible. Heuristics are added in order to speed up the computation process and to take into account some practical aspects of power systems operation. A special procedure is carried out in case of critical situations, where emergency control actions are defined. The method's general idea is to keep the new secure operating point as close as possible to the original one while minimizing the amount of load shedding. The method can be a helpful tool for operation planning studies, security analysis, and reliability evaluation Of power systems. Simulations have been carried out for small test to large real life systems in order to show the effectiveness of the proposed method.
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