Optimization

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Chapter 3
OPTIMIZATION

3.1 Introduction
Optimization is a chronic and natural process usually witnessed in our daily life events. In various disciplines such as engineering designs, manufacturing systems, agricultural sciences, physical sciences, economics, pattern recognition etc. optimization is observed. Optimization is, thus a process of making best, effective and functional solution out of possible choices no way differs from the structural optimization which is being conceived in the present work. Structural optimization is a decisive and tricky step, where the designers are able to generate better designs saving time and money. Conventional optimization approaches like mathematical programming method, optimality criteria method etc. fails miserably in structural design problems which are highly complex and time consuming in nature. Optimization problems are mathematical models formulated to solve complex designs that may be of multi-objective nature in certain cases. Structural design procedure involves conceptual design and design realization leading to several probable results since higher degree of ambiguity is experienced in every steps. Conceptual design phase is more dependent on decision variables than in advance optimization phase. Optimization is one of the major tool for decision making at the conceptual or realization phase of modern design techniques. In design realization stage optimization is achieved by mathematical and numerical search methods.
Extensive ranges of mathematical programming methods with higher potentials are available for solving wide varieties of engineering optimization especially for structural design problems. These practical structural optimization problems considers discrete mathematic...

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...ciated with its parallel processing capability. To solve optimization problems, neural computing techniques have been adopted in the recent past. A neural network is a massively parallel network of interconnected simple processors (neurons) in which each neuron accepts a set of inputs from other neurons and computes an output that is propagated to the output nodes. Thus a neural network can be described in terms of the network connectivity, individual neurons, the weights associated with the interconnections between neurons and the activation function of each neuron. The network maps an input vector from one space to another. In case of the network mapping it is not specified but is learned.

Out of these methods of optimization, mostly chosen and the one chosen in the present study is genetic algorithm, a detailed discussion on which is been given in chapter 4.

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