Research Area:  Metaheuristic Computing

Abstract:

The key idea underlying iterated local search is to focus the search not on the full space of all candidate solutions but on the solutions that are returned by some underlying algorithm, typically a local search heuristic. The resulting search behavior can be characterized as iteratively building a chain of solutions of this embedded algorithm. The result is also a conceptually simple metaheuristic that nevertheless has led to state-of-the-art algorithms for many computationally hard problems. In fact, very good performance is often already obtained by rather straightforward implementations of the metaheuristic. In addition, the modular architecture of iterated local search makes it very suitable for an algorithm engineering approach where, progressively, the algorithm’s performance can be further optimized. Our purpose here is to give an accessible description of the underlying principles of iterated local search and a discussion of the main aspects that need to be taken into account for a successful application of it. In addition, we review the most important applications of this method and discuss its relationship with other metaheuristics.

Keywords:  
Iterated Local Search (ILS)
Single Machine Total Weighted Tardiness Problem (SMTWTP)
Large-step Markov Chains (LSMC)
Random Restarts
Permutation Flow Shop Problem (PFSP)

Author(s) Name:   Helena Ramalhinho Lourenço, Olivier C. Martin & Thomas Stützle

Publisher name:  Springer

DOI:  10.1007/978-3-319-91086-4_5

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-3-319-91086-4_5