The paper presents the cost optimization design of simply supported reinforced concrete beams of rectangular cross-section reinforced with tension reinforcement. The optimization was formulated as finding the minimum ...
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The analytical target cascading (ATC) methodology for optimizing hierarchical systems has demonstrated convergence properties for continuous, convex formulations. However, many practical problems involve both continuo...
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The selection process of key engine design variables to maximize peak power subject to fuel economy and packaging objectives is formulated as an optimization problem readily solved with nonlinear programming. The meri...
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nonlinear programming is applied to identifier design for linear system. How to choose the gradient method and quadratic cost function is discussed. New concept of time-varying nonlinear programming is introduced. ...
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In this paper, on the basis of the existing bank lending schemes, first quantitatively from the enterprise strength, corporate reputation and the stability of supply and demand of three selected 6 most representative ...
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This paper provides a non-interior homotopy method for nonlinear programming with general equality and inequality constraints. Under a weak assumption for a swelled feasible set as well as several basic conditions for...
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This paper introduces a new technique for designing nonlinear feedback controllers that can effectively and efficiently control nonlinear and unstable dynamical systems. The technique, called State-Parameterized Nonli...
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The sensor location problem leads to a circle packing problem which is also an interesting challenge of the quadratically constrained nonlinear programming. In this paper we proposed a simple approach to give approxim...
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By redefining multiplier associated with inequality constraint as a positive definite function of the originally-defined multiplier, u i2, i = 1, 2,,m, say, the nonnegative constraints imposed on inequality constraint...
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Genetic algorithms have been shown to be robust optimization algorithms for real-value functions defined over domains of the form Rn (R denotes the real number). But there exist some obstacles in genetic algorithms su...
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Genetic algorithms have been shown to be robust optimization algorithms for real-value functions defined over domains of the form Rn (R denotes the real number). But there exist some obstacles in genetic algorithms such as premature convergence and slow convergence speed. In this paper, a new approach called Hybrid Genetic Algorithms (HGA) is presented to overcome them for the nonlinear programming through combining the genetic algorithms with the feasible path method after introducing a new operator called learning operator. Finally, the validity of the approach is illustrated by providing HGA for nonlinear programming.
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