Practical optimization using evolutionary methods

Many real-world problem solving tasks, including CFD problems, involve posing and solving optimization problems, which are usually non-linear, non-differentiable, multi-dimensional, multi-modal, stochastic, and computationally time-consuming. In this paper, we discuss a number of such practical problems which are, in essence, optimization problems and review the classical optimization methods to show that they are not adequate in solving such demanding tasks. On the other hand, in the past couple of decades, new yet practical optimization methods, based on natural evolutionary techniques, are increasingly found to be useful in meeting the challenges. These methods are population based, stochastic, and exible, thereby providing an ideal platform to modify them to suit to solve most optimization problems. The remainder of the paper illustrates the working principles of such evolutionary optimization methods and presents some results in support of their efficacy. The breadth of their application domain and ease and efficiency of their working make evolutionary optimization methods promising for taking up the challenges offered by the vagaries of various practical optimization problems.

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