ABSTRACT
This chapter presents an introductory description on statistical thermodynamics and process of annealing. It explains the notion of optimization based on simulated annealing (SA) and different annealing or cooling schedules used in SA and neighborhoods of solutions. The chapter also presents variants of SA algorithm such as improved and modified variants and hybrids of SA, and convergence analysis of SA algorithms. It finally presents some recent applications of SA of selected benchmark and engineering problems. The basic idea of SA algorithm came from statistical thermodynamics, a branch of physics, where a thermal equilibrium is sought to ensure global minimum energy that gives the best properties of metal. In condensed matter physics, annealing is known as a thermal process where low energy of a solid in bath is obtained. Generic annealing schedules can be broadly categorized into three classes: monotonic schedules, geometric schedules and adaptive schedules.
