ikkurthis1998 / Simulated-Annealing-Python

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.

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.