Design of cooperative algorithms for multi-objective optimization: application to the flow-shop scheduling problem

This is a summary of the main results presented in the author’s PhD thesis. This thesis was supervised by El-Ghazali Talbi, and defended on 21 June 2005 at the University of Lille (France). It is written in French and is available at http://www.lifl.fr/~basseur/These.pdf. This work deals with the conception of cooperative methods in order to solve multi-objective combinatorial optimization problems. Many cooperation schemes between exact and/or heuristic methods have been proposed in the literature. We propose a classification of such schemes. We propose a new heuristic called adaptive genetic algorithm (AGA), that is designed for an efficient exploration of the search space. We consider several cooperation schemes between AGA and other methods (exact or heuristic). The performance of these schemes are tested on a bi-objective permutation flow-shop scheduling problem, in order to evaluate the interest of each type of cooperation

Hill-Climbing Behavior on Quantized NK-Landscapes

This paper provides guidelines to design climbers considering a landscape shape under study. In particular, we aim at competing best improvement and first improvement strategies, as well as evaluating the behavior of different neutral move policies. Some conclusions are assessed by an empirical analysis on non-neutral (NK-) and neutral (quantized NK-) landscapes. Experiments show the ability of first improvement to explore rugged landscapes, as well as the interest of accepting neutral moves at each step of the search

Métaheuristiques pour l'ordonnancement biobjectif de type flowshop

Pour assurer une production de biens de qualité, de manière fiable et dans des délais maîtrisés, les organisations ont besoin d’outils d\u27exécution optimale de tâches tels que l’ordonnancement. Le succès des méthodologies de résolution des problèmes d’ordonnancement de production basées sur les métaheuristiques s’explique par leur capacité à fournir des solutions proches de l’optimum, dans des temps raisonnables. Cet ouvrage se consacre, dans un premier temps, aux métaheuristiques appliquées aux problèmes d’ordonnancement multicritère, qui sont des cas particuliers des problèmes d’optimisation combinatoire multicritère, généralement NP-difficiles. Puis, il s’intéresse aux préoccupations d’ordonnancement dans le secteur du transport qui suscitent également de multiples problèmes d’optimisation. Deux grands domaines d’application se distinguent, celui des systèmes de transport et celui des ressources de transport intervenant dans un atelier

Toward an Efficient Exploration of Fitness Landscapes

Within local search algorithms, descent methods are rarely studied experimentally. However, these search techniques are the basis of many modern metaheuristics and have an influence on the ability of an algorithm to achieve good solutions of a fitness landscape. Through a large empirical study of classic runs, we show that certain ideas about descents methods are false. These results indicate that it is possible to make a descent ’intelligent’ and lead to better solutions, regardless of the problem addressed

Sampled Walk and Binary Fitness Landscapes Exploration

In this paper we present and investigate partial neighborhood local searches, which only explore a sample of the neighborhood at each step of the search. We particularly focus on establishing link between the structure of optimization problems and the efficiency of such local search algorithms. In our experiments we compare partial neighborhood local searches to state-of-the-art tabu search and iterated local search and perform a parameter sensitivity analysis by observing the efficiency of partial neighborhood local searches with different size of neighborhood sample. In order to facilitate the extraction of links between instances structure and search algorithm behavior we restrain the scope to binary fitness landscapes, such as NK landscapes and landscapes derived from UBQP