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This python code is developed by Sreemannarayana Ikkurthi, as a part of course notes for the course 15AES477: Multidisciplinary Design Optimization (MDO). In support of Dr. Rajesh Senthil Kumar T., Assistant Professor, Department of Aerospace Engneering, Amrita Vishwa Vidyapeetham.

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README.md

Simulated-Annealing

This code is developed by Sreemannarayana Ikkurthi, as a part of course notes for the course 15AES477: Multidisciplinary Design Optimization (MDO), in the year 2019-20

In support of Dr. Rajesh Senthil Kumar T., Assistant Professor,

Department of Aerospace Engneering, Amrita Vishwa Vidyapeetham.

About Simulated Annealing

Simulated Annealing is an optimization method, mimicing annealing process.

A detailed explanation about the method can be found in the text book:

Deb Kalyanmoy, Optimization for Engineering Design, Algorithms and Examples. (2012)

About Code

This code can be used to optimize an objective function of n variables and produce a contour plots of adjacent variables of all generations.

min_x, min_f, good_x = Sim_Ann(ini_x, fac, T, t_fac, n_fac):

    Inputs:
          ini_x = Initial search point
          fac = Search factor
          T = Temperature
          t_fac = Temperature factor
          n_fac = n factor
    Outputs:
          min_x = Minima point
          min_f = Minimum function value
          good_x = List of good points
    Usage: 
          Get minima point and its function value.

Give appropriate objective function and input variables in INPUT ARENA to get the minima and plot contours.

About

This python code is developed by Sreemannarayana Ikkurthi, as a part of course notes for the course 15AES477: Multidisciplinary Design Optimization (MDO). In support of Dr. Rajesh Senthil Kumar T., Assistant Professor, Department of Aerospace Engneering, Amrita Vishwa Vidyapeetham.

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