Question

In Simulated Annealing, why is it necessary to occasionally accept a solution that is worse than the current one early in the search process?

Accepted Answer

Simulated Annealing is an optimization algorithm used to find the global optimum, which is the best possible solution to a problem. The algorithm encounters local optima, which are solutions that appear better than their immediate neighbors but are inferior to the true global optimum. If an algorithm only accepts moves that improve the current solution, it becomes trapped in these local optima, effectively ending the search prematurely. By occasionally accepting worse solutions, the algorithm can climb out of a local optimum and explore different regions of the search space. This process is controlled by a parameter called temperature, which represents the probability of accepting a worse solution. Early in the search, the temperature is high, allowing for significant exploration and frequent acceptance of worse moves. This behavior mimics the physical process of annealing in metallurgy, where heating a metal allows atoms to move freely before settling into a stable, low-energy structure as it cools. As the algorithm progresses, the temperature is gradually decreased, a process known as cooling, which makes the algorithm less likely to accept worse solutions and more focused on refining the current path toward the global optimum. Without the ability to accept worse solutions early on, the algorithm would be limited to a small area of the search landscape, missing potentially superior solutions located across barriers of lower-quality states.

Home → All Courses → Science and Mathematics Courses → Decision Science and Optimization → Flashcard

In Simulated Annealing, why is it necessary to occasionally accept a solution that is worse than the current one early in the search process?