Practical Considerations for Simulated Annealing Implementation

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Abstract

Nowadays, there are many optimization problems where exact methods do not exist or where deterministic methods are computationally too complex to implement. Simulated annealing may be the answer for these cases. It is not greedy in the sense that it is not fool with false minima, and is pretty easy to implement. Furthermore, because it does not require a mathematical model, it can be used to solve a broad range of problems. Unfortunately, mapping a real problem to the domain of simulated annealing can be difficult and requires familiarity with the algorithm. More often than not, it is possible to encode the solution (solve the problem) using several approaches. In addition, there are other factors that determine the success of failure of this algorithm. This chapter reviews how to plan the encoding of the solution, and discusses how to decide which encoding is more appropriate for each application. Several practical considerations for the proper implementation of simulated annealing are reviewed and analyzed. These include how to perturb the solution, how to decide a proper cooling schedule, and most important, how to properly implement the algorithm. Several cooling schedules are covered, including exponential, linear and temperature cycling. Additionally, the impact of random number generators is examined; how they affect the speed and quality of the algorithm. Essentially, this chapter is focused for those who want to solve real problems using simulated annealing for artificial intelligence, engineering, or research. An illustrative example is solved using simulated annealing and implemented in a popular programming language using an object-oriented approach. This chapter offers a great opportunity to understand the power of this algorithm as well as to appreciate its limitations. Finally, it is reviewed how is possible to combine simulated annealing with other optimization algorithms (including the deterministic ones) to solve complex optimization problems. In particular, it is discussed how to train artificial neural networks using simulated annealing with gradient based algorithms.

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