5 Simulated Annealing

This chapter covers

Introducing trajectory-based optimization algorithms
Understanding simulated annealing algorithm
Solving function optimization as an example of continuous optimization problems
Solving puzzle game problem like Sudoku as an example of constraint satisfaction problems
Solving permutation problems like TSP as an example of discrete problems
Solving real-world delivery semi-truck routing problem

In this chapter, simulated annealing is presented and discussed as a trajectory-based metaheuristic optimization technique. Different elements of this algorithm are described. Adaptation aspects of simulated annealing are also highlighted. In this chapter, a number of case studies are presented to show the ability of this metaheuristic algorithm in solving continuous and discrete optimization problems.

5.1 Introduction to Trajectory-based Optimization

5.2 Simulated Annealing Algorithm

5.2.1 Physical Annealing

5.2.2 SA Pseudocode

5.2.3 Transition Probability

5.2.4 SA Cooling Schedules

5.2.5 Initial Temperature

5.2.6 Final Temperature

5.2.7 Annealing Schedule or Temperature Decrement

5.2.8 Iterations at each temperature

5.2.9 Adaptation in SA

5.3 Function Optimization

5.4 Solving Sudoku

5.5 Solving TSP

5.6 Solving Delivery Semi-Truck Routing Problem

5.7 Exercises

5.8 Summary