A New Hybrid Method to Solve the Multi-objective Optimization Problem for a Composite Hat-Stiffened Panel

Abstract

Algorithm portfolios are known to offer robust performances, efficiently overcoming the weakness of every single algorithm on some particular problem instances. Two complementary approaches to get the best out of an algorithm portfolio is to achieve algorithm selection (AS), and to define a scheduler, sequentially launching a few algorithms on a limited computational budget each. The presented Algorithm Selector And Prescheduler system relies on the joint optimization of a pre-scheduler and a per instance AS, selecting an algorithm well-suited to the problem instance at hand. ASAP has been thoroughly evaluated against the state-of-the-art during the ICON challenge for algorithm selection, receiving an honourable mention. Its evaluation on several combinatorial optimization benchmarks exposes surprisingly good results of the simple heuristics used; some extensions thereof are presented and discussed in the paper.This research work has been funded by the French Program "Investissements d'Avenir". 4 One often considers the joint problems of selecting an algorithm and the optimal hyper-parameters thereof, referred to as Algorithm Configuration (AC), as the choice of the hyper-parameter values governs the algorithm performance. AC is outside the scope of the paper and will not be further considered.2 sciencesconf.org:meta2016:108942 and reproducible benchmarking of AS approaches on 13 domains ranging from satisfiability to operations research (section 4).The comparative empirical validation of ASAP demonstrates its good performances comparatively to state-of-art pre-schedulers and AS approaches (section 5), and its complementarity with respect to the prominent Zilla algorithms [22]. The paper concludes with a discussion of the limitations of the ASAP approach, and some perspectives for further research.