Simulated Annealing Algorithm Example with Implementation Guide

In this article, we present a comprehensive example of the simulated annealing algorithm, delving into its specific procedural steps and underlying principles. Simulated annealing is a Monte Carlo-based optimization technique inspired by the physical annealing process in solid-state physics. The algorithm mimics the gradual cooling of materials to find global optima within solution spaces. Our discussion begins with the algorithm's origins and fundamental concepts, progressively exploring its practical implementation through pseudo-code and key parameter considerations (such as temperature schedules and acceptance probability functions). We demonstrate core computational components including neighbor solution generation, energy evaluation functions, and the metropolis criterion for accepting suboptimal solutions. Additionally, we provide real-world application case studies with code snippets to help readers better understand and apply this algorithm in optimization scenarios.