Global convergence to first-order critical points is proved for a variant of the trust-region SQP-filter algorithm analyzed in (Fletcher, Gould, Leyffer and Toint). This variant allows the use of two types of step str...
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A method for solving nonlinear programming using genetic algorithm is presented. In the operations of crossover and mutation in each generation, to ensure the new solutions are all feasible, we present a method in whi...
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A method for solving nonlinear programming using genetic algorithm is presented. In the operations of crossover and mutation in each generation, to ensure the new solutions are all feasible, we present a method in which the bounds of every variable in the solution are estimated beforehand according to the constrained conditions. For the operation of mutation, we present two methods of cube bounding and variable bounding. The experimental results are given and analyzed. They show that the method is efficient and can obtain the results in less generation.
To remain competitive firms need to develop long-term strategies for acquiring and using advanced engineering and manufacturing technologies. However, there are innumerable alternatives for obtaining internally and/or...
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To remain competitive firms need to develop long-term strategies for acquiring and using advanced engineering and manufacturing technologies. However, there are innumerable alternatives for obtaining internally and/or externally the personnel and equipment resources needed to employ advanced technologies. A mixed integer, nonlinear programing model (MINP/L) is presented which identifies the optimal alternative for long-term capacity and acquisition planning for advanced technology resource requirements while allowing the personnel and equipment to have realistic nonlinear performance improvement (learning) or decay capabilities. A example problem is provided and an optimal solution is obtained and compared to the results of more common, but also more expensive, solutions. Numerous future research extensions are offered.
A general method for the optimum design of gear boxes is presented using a nonlinear programming technique. The mathematical model of the gear box is developed using a symbolic mathematical manipulator. Constraints of...
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This work examines the feasibility of using a direct solution method to the fixed-trim reentry vehicle optimal control problem as part of a reentry guidance scheme. A procedure was developed to calculate locally optim...
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The aim of this paper is to propose a novel neural network model based on differential-algebraic equations for nonlinear programming. The penalty function method or barrier function method are used to convert a constr...
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Numerical methods are outlined for the continuation, solution type determination, singularity detection, and bifurcation in parametric constrained optimization problems. The problem is first converted to an ‘ac...
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The identification of the Quadratic in-the-State Systems (QSS) is the objective of this paper. This class, on its own, enjoys a useful model for many nonlinear dynamic systems. Moreover, it represents a bilinear syste...
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Micro, Small and Medium Enterprises (MSMEs) play an important role in economic development However, due to the prevalence of information asymmetry, MSMEs are hard to borrow money and banks have difficulties in accurat...
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In this paper, we propose a novel trajectory-based methodology for systematically computing multiple optimal solutions of general nonlinear programming problems. The objective functions are assumed to be twice-differe...
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ISBN:
(纸本)0780366859
In this paper, we propose a novel trajectory-based methodology for systematically computing multiple optimal solutions of general nonlinear programming problems. The objective functions are assumed to be twice-differentiable and the feasible region may be non-convex and disconnected. A theoretical foundation of the methods is made on the basis of the theory of differential topology and the qualitative theory of dynamical systems. Our proposed method begins with an arbitrary initial point and consists of two distinct main phases: Phase I systematically finds several or all of the different connected feasible regions from the initial point. Phase II then finds multiple or all of the local minima in each feasible region obtained in Phase I. A numerical example is shown to illustrate the proposed method.
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