Simulated Annealing

Simulated annealing is an optimization technique used to find approximate solutions to difficult problems. The technique models the process of heating a solid material, such as metal, and then slowly cooling it. As the material cools, the molecules slow down and settle into the lowest energy state they can reach, just like real-world annealing. Simulated annealing works by mimicking this same process to find low energy solutions to complex problems. The algorithm works by making random changes, or mutations, to the solution and seeing whether the energy of the solution is increased or decreased. If the energy is decreased, the change is kept, otherwise the change is rejected, just as molecules will only remain in a lower energy state. By repeating this process, an approximate solution can be found. Simulated annealing has many uses, including in complex scheduling, resource allocation, and data compression problems. This technique is becoming increasingly important due to its ability to find reasonable solutions quickly and efficiently.