Search algorithms are very important for artificial intelligence since they help solve hard issues by using optimization and decision-making methods. This study gives a thorough and critical look at a number of search algorithms, with a special focus on Simulated Annealing because it is flexible and works well for solving combinatorial optimization problems. This study looks at current literature in a methodical way to look at the problems that Simulated Annealing tries to solve, the methods it uses, the experiments it does, the findings it gets, and how useful they are in real life. The topic includes both positive aspects, such algorithmic efficiency, accuracy, global optimization potential, and adaptability across different fields, as well as negative aspects, like convergence problems, parameter sensitivity, and computational complexity. This study points out areas where Simulated Annealing could be better and suggests areas for further research. It also emphasizes the possibilities of combining Simulated Annealing with other Meta heuristic methods to improve performance and use. The main goal of this in-depth study is to help researchers and practitioners choose, modify, and use the right search algorithms for the specific needs and features of different optimization issues.
Keywords: Simulated annealing, JSP, TSP, Retention Strategy, Amigobot, Search Algorithms