Simulated Annealing Algorithm Implementation Guide

In this article, we provide a detailed discussion on implementing an efficient simulated annealing algorithm. When coding this algorithm, developers must consider several critical parameters including initial temperature, temperature decay coefficient, and iteration count. The implementation typically involves defining an objective function to evaluate solution quality and establishing a neighbor generation mechanism for solution exploration. We also examine strategies for selecting initial solutions and updating solutions during each iteration through probability-based acceptance criteria that allows occasional acceptance of worse solutions to escape local optima. To enhance algorithm performance, we discuss performance evaluation using metrics like convergence speed, solution quality, and computational efficiency. The article further provides optimization techniques such as adaptive cooling schedules, parallel computation approaches, and memory mechanisms like reheating. This guide aims to help developers deeply understand simulated annealing algorithms and achieve better results in practical applications through proper parameter tuning and implementation best practices.