EASE-OF-USE, IMPLEMENTATION AND PERFORMANCE OF HEURISTICS FOR OPTIMIZATION — A COMPARISON OF EVOLUTIONARY AND ITERATIVE IMPROVEMENT METHODS
Kuldeep Singh, Karl E. Kurbel · International Journal of Artificial Intelligence Tools · 2001
Many new approaches to solve optimization problems have been proposed with the appearance of methods based on analogies to natural processes. For the user in practice, it is not only difficult to assess which method is the "best" one but also to judge how easy or complicated it is to implement and apply the methods. This is mainly due to the fact that most methods have interacting parameters which need to be tuned before a method can be used. The paper discusses ease of use and implementation as well as performance aspects based on computational tests. Thre iterative improvement methods (simulated annealing, tabu search, threshold accepting) and two evolutionary methods (genetic algorithms, PRSA) are compared with regard to an optimization problem (standard cell placement in chip design). Based on results from a large number of test runs, we discuss how easy or difficult it is to prepare the methods for a specific application. In summary, iterative improvement methods were found easier to implement and to use than evolutionary methods. They also outperformed the latter ones in terms of solution quality and computing time.