Local Search: Hill Climbing With Simulated Anealing Part-6

[@suketudave7508]

Hats off to Prof. Mausam. Since past so many days I was searching the internet for a perfect, simple-to-understand explanation of Simulated Annealing. Finally this video helped me to understand end-to-end, The approach of explanation is so fantastic. Thanks!!

[@vetrimaaran1513]

Analogy: Imagine you're at a large outdoor flea market looking for the best antique vase. You have a limited amount of time, so you can't look at every single item. Instead, you start at a random booth and check the vase there. - **High Temperature (Beginning of Search):** You start with a lot of energy and enthusiasm, so you're willing to wander to booths far away from your starting point, not worrying too much about whether each new booth seems better or worse than the last one. You're exploring widely. - **Acceptance of Moves:** Every time you find a new vase, you decide whether to keep it or look for another. At first, even if a new vase is worse than the one you have, you might still take it, hoping that it leads you to an even better part of the market. - **Cooling Schedule (Decreasing Temperature):** As time goes on, you start to tire, so you become more selective about moving to a new booth, especially if it means giving up a nice vase you've already found. Your "willingness to explore" decreases like the cooling temperature in simulated annealing. - **Low Temperature (End of Search):** Towards the end of your time at the market, you're only willing to move to a new booth if you see a vase that's clearly better than the one you currently have. You're refining your search, focusing on the best area you found. - **Final Choice:** When it's almost time to leave, you settle on the best vase you've found so far, which is likely one of the better ones available in the time you had, even if not the absolute best in the entire market. In this analogy, the market is the problem space, the vases are potential solutions, and your energy and time represent the temperature and cooling schedule of the simulated annealing algorithm. The process of becoming more selective about which new booth to visit parallels the algorithm's decreasing likelihood of accepting worse solutions as it gets closer to the end of its run.