MOParamILS : une plateforme multi-objectif pour la configuration automatique d'algorithmes

International audienceDans le domaine de l'optimisation combinatoire, où les problèmes sont souvent NP-durs, les métaheuristiques et autres algorithmes d'approximations ont souvent de nombreux paramètres pour pouvoir s'adapter à une large gamme de scénarios. Très souvent, trouver de bonnes valeurs pour ces paramètres est une tâche manuelle longue et fastidieuse car leurs implications et interactions sont généralement complexes et non intuitives. À ce jour, la majorité des configurateurs automatique de la littérature (GGA, SMAC, ParamILS, I/F-Race) se concentrent à optimiser les performances d'un algorithme vis-à-vis d'un critère unique. Après S-Race et SPRINT-Race, extensions multi-objectif récentes de F-Race, nous introduisons MOParamILS, une extension de ParamILS pour le problème de configuration automatique multi-critère

MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

International audienceAutomated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator

Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case

Simulation optimisation offers great opportunities in the design and optimisation of complex systems. In the presence of multiple objectives, there is usually no single solution that performs best on all objectives. Instead, there are several Pareto-optimal (efficient) solutions with different trade-offs which cannot be improved in any objective without sacrificing performance in another objective. For the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via simulation, we consider the problem of efficiently identifying the Pareto-optimal designs out of a (small) given set of alternatives. We present a simple myopic budget allocation algorithm for multi-objective problems and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. This paper shows how the algorithm works in bi-objective problems under different settings. Empirical tests show that our algorithm can significantly reduce the necessary simulation budget

Automatic Design of Multi-Objective Local Search Algorithms: Case Study on a bi-objective Permutation Flowshop Scheduling Problem

International audienceMulti-objective local search (MOLS) algorithms are efficient metaheuristics, which improve a set of solutions by using their neighbourhood to iteratively find better and better solutions. MOLS algorithms are versatile algorithms with many available strategies, first to select the solutions to explore, then to explore them, and finally to update the archive using some of the visited neighbours. In this paper, we propose a new generalisation of MOLS algorithms incorporating new recent ideas and algorithms. To be able to instantiate the many MOLS algorithms of the literature, our generalisation exposes numerous numerical and categorical parameters, raising the possibility of being automatically designed by an automatic algorithm configuration (AAC) mechanism. We investigate the worth of such an automatic design of MOLS algorithms using MO-ParamILS, a multi-objective AAC configurator, on the permutation flowshop scheduling problem, and demonstrate its worth against a traditional manual design

Réagir et s’adapter à son environnement: Concevoir des méthodes autonomes pour l’optimisation combinatoire à plusieurs objectifs

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself.While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms.We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.Les problèmes d’optimisation à grande échelle sont généralement difficiles à résoudre de façon optimale. Des algorithmes d’approximation tels que les métaheuristiques, capables de trouver rapidement des solutions sous-optimales, sont souvent préférés. Cette thèse porte sur les algorithmes de recherche locale multi-objectif (MOLS), des métaheuristiques capables de traiter l’optimisation simultanée de plusieurs critères. Comme de nombreux algorithmes, les MOLS exposent de nombreux paramètres qui ont un impact important sur leurs performances. Ces paramètres peuvent être soit prédits et définis avant l’exécution de l’algorithme, soit ensuite modifiés dynamiquement.Alors que de nombreux progrès ont récemment été réalisés pour la conception automatique d’algorithmes, la grande majorité d’entre eux ne traitent que d’algorithmes mono-objectif et l’optimisation d’un unique indicateur de performance. Dans cette thèse, nous étudions les relations entre la conception automatique d’algorithmes et l’optimisation multi-objective.Nous passons d’abord en revue les stratégies MOLS possibles et présentons un framework MOLS général et hautement configurable. Nous proposons également MO-ParamILS, un configurateur automatique spécialement conçu pour gérer plusieurs indicateurs de performance. Nous menons ensuite plusieurs études sur la conception automatique de MOLS sur de multiples problèmes combinatoires bi-objectifs. Enfin, nous discutons deux extensions de la configuration d’algorithme classique : d’abord l’intégration des mécanismes de contrôle de paramètres, pour bénéficier de multiples prédictions de configuration; puis l’utilisation séquentielle de plusieurs configurations

Sprint Multi-Objective Model Racing

Multi-objective model selection, which is an important aspect of Machine Learning, refers to the problem of identifying a set of Pareto optimal models from a given ensemble of models. This paper proposes SPRINT-Race, a multiobjective racing algorithm based on the Sequential Probability Ratio Test with an Indifference Zone. In SPRINT-Race, a non-parametric ternary-decision sequential analogue of the sign test is adopted to identify pair-wise dominance and non-dominance relationship. In addition, a Bonferroni approach is employed to control the overall probability of any erroneous decisions. In the fixed confidence setting, SPRINT-Race tries to minimize the computational effort needed to achieve a predefined confidence about the quality of the returned models. The efficiency of SPRINT-Race is analyzed on artificially-constructed multi-objective model selection problems with known ground-truth. Moreover, SPRINT-Race is applied to identifying the Pareto optimal parameter settings of Ant Colony Optimization algorithms in the context of solving Traveling Salesman Problems. The experimental results confirm the advantages of SPRINT-Race for multi-objective model selection