Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems - Pagnozzi, Federico; Stützle, Thomas (European journal of operational research, 2019)
•We present a system for the automatic design of Hybrid Stochastic Local Searches.•Algorithms are obtained by combining components following rules defined by a grammar.•The system is tested on three major objectives of the permutation flowshop problem.•The results show that the generated algorithms outperform the state of the art. Stochastic local search methods are at the core of many effective heuristics for tackling different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful, manual algorithm engineering effort to reach high performance. An alternative to the manual algorithm engineering is the automated design of effective SLS algorithms through building flexible algorithm frameworks and using automatic algorithm configuration techniques to instantiate high-performing algorithms. In this paper, we automatically generate new high-performing algorithms for some of the most widely studied variants of the PFSP. More in detail, we (i) developed a new algorithm framework, EMILI, that implements algorithm-specific and problem-specific building blocks; (ii) define the rules of how to compose algorithms from the building blocks; and (iii) employ an automatic algorithm configuration tool to search for high performing algorithm configurations. With these ingredients, we automatically generate algorithms for the PFSP with the objectives makespan, total completion time and total tardiness, which outperform the best algorithms obtained by a manual algorithm engineering process.