9 research outputs found
LNS Crossover Operator for the GTSP A Memetic Algorithm with a Large Neighborhood Crossover Operator for the Generalized Traveling Salesman Problem
Méthode de Branch & Price heuristique appliquée au problÚme de tournées de véhicules avec contraintes de chargement.
A memetic algorithm with a large neighborhood crossover operator for the generalized traveling salesman problem
International audienceThe Generalized Traveling Salesman Problem (GTSP) is a generalization of the well-known Traveling Salesman Problem (TSP), in which the set of nodes is divided into mutually exclusive clusters. The objective of the GTSP consists in visiting each cluster exactly once in a tour, while minimizing the sum of the routing costs. This paper addresses the solution of the GTSP using a Memetic Algorithm procedure. The originality of our approach rests on the crossover procedure that uses a large neighborhood search. This algorithm is compared with other algorithms on a set of 41 standard test problems with up to 442 nodes. The obtained results show that our algorithm is efficient in both solution quality and computation time
A memetic algorithm with a large neighborhood crossover operator for the generalized traveling salesman problem
International audienc
Large Neighborhood Search for Variants of TSP
International audienceTo solve problems with Local Search procedures, neighborhoods have to be defined. During the resolution, a solution is typically replaced by the best solution found in its neighborhood. A question concerns the size of the neighborhood. If a small neighborhood can be explored in polynomial time, a large neighborhood search may bring a faster convergence to a local optimum of good quality. In this paper, we propose a class of large neighborhood search which can be implemented on some extensions of the Traveling Salesman Problem
Techniques hybrides de recherche exacte et approchée (application à des problÚmes de transport)
Nous nous intĂ©ressons dans cette thĂšse aux possibilitĂ©s d hybridation entre les mĂ©thodes exactes et les mĂ©thodes heuristiques afin de pouvoir tirer avantage de chacune des deux approches : optimalitĂ© de la rĂ©solution exacte, caractĂšre moins dĂ©terministe et rapiditĂ© de la composante heuristique. Dans l objectif de rĂ©soudre des problĂšmes NPdifficiles de taille relativement importante tels que les problĂšmes de transports, nous nous intĂ©ressons dans les deux derniĂšres parties de ce mĂ©moire Ă la conception de mĂ©thodes incomplĂštes basĂ©es sur ces hybridations. Dans la premiĂšre partie, nous allons nous intĂ©resser aux mĂ©thodes de rĂ©solution par recherche arborescente. Nous introduisons une nouvelle approche pour la gestion des dĂ©cisions de branchement, que nous appelons Dynamic Learning Search (DLS). Cette mĂ©thode dĂ©finit de maniĂšre dynamique des rĂšgles de prioritĂ© pour la sĂ©lection des variables Ă chaque noeud et l ordre des valeurs sur lesquelles brancher. Ces rĂšgles sont conçues dans une optique de gĂ©nĂ©ricitĂ©, de maniĂšre Ă pouvoir utiliser la mĂ©thode indĂ©pendamment du problĂšme traitĂ©. Le principe gĂ©nĂ©ral est de tenir compte par une technique d apprentissage de l impact qu ont eu les dĂ©cisions de branchement dans les parties dĂ©jĂ explorĂ©es de l arbre. Nous Ă©valuons l efficacitĂ© de la mĂ©thode proposĂ©e sur deux problĂšmes classiques : un problĂšme d optimisation combinatoire et un problĂšme Ă satisfaction de contraintes. La deuxiĂšme partie de ce mĂ©moire traite des recherches Ă grand voisinage. Nous prĂ©sentons un nouvel opĂ©rateur de voisinage, qui dĂ©termine par un algorithme de programmation dynamique la sous-sĂ©quence optimale d un chemin dans un graphe. Nous montrons que cet opĂ©rateur est tout particuliĂšrement destinĂ© Ă des problĂšmes de tournĂ©es pour lesquels tous les noeuds ne nĂ©cessitent pas d ĂȘtre visitĂ©s. Nous appelons cette classe de problĂšme les ProblĂšmes de TournĂ©es avec Couverture Partielle et prĂ©sentons quelques problĂšmes faisant partie de cette classe. Les chapitres 3 et 4 montrent, Ă travers des tests expĂ©rimentaux consĂ©quents, l efficacitĂ© de l opĂ©rateur que nous proposons en appliquant cette recherche Ă voisinage large sur deux problĂšmes, respectivement le ProblĂšme de l Acheteur ItinĂ©rant (TPP) et le ProblĂšme de Voyageur de Commerce GĂ©nĂ©ralisĂ© (GTSP). Nous montrons alors que cet opĂ©rateur peut ĂȘtre combinĂ© de maniĂšre efficace avec des mĂ©taheuristiques classiques, telles que des algorithmes gĂ©nĂ©tiques ou des algorithmes d Optimisation par Colonies de Fourmis. Enfin, la troisiĂšme partie prĂ©sente des mĂ©thodes heuristiques basĂ©es sur un algorithme de GĂ©nĂ©ration de Colonnes. Ces mĂ©thodes sont appliquĂ©es sur un problĂšme complexe : le problĂšme de TournĂ©es de VĂ©hicules avec Contraintes de Chargement Ă Deux Dimensions (2L-VRP). Nous montrons une partie des possibilitĂ©s qu il existe afin de modifier une mĂ©thode a priori exacte en une mĂ©thode heuristique et nous Ă©valuons ces possibilitĂ©s Ă l aide de tests expĂ©rimentauxWe are interested in this thesis in the possibilities of hybridization between the exact methods and the methods heuristics to be able to take advantage of each of both approaches: optimality of the exact resolution, the less determinist character and the speed of the constituent heuristics. In the objective to resolve problems NP-hard of relatively important size such as the transportation problems, we are interested in the last two parts of this report in the conception of incomplete methods based on these hybridizations. In the first part, we are going to be interested in the methods of resolution by tree search. We introduce a new approach for the management of the decisions of connection, which we call Dynamic Learning Search ( DLS). This method defines in a dynamic way rules of priority for the selection of variables in every knot and the order of the values on which to connect. These rules are conceived in an optics of genericity, so as to be able to use the method independently of the treated problem. The general principle is to take into account by a technique of learning of the impact which had the decisions of connection in the parts already investigated in the tree. We estimate the efficiency of the method proposed on two classic problems: a combinatorial optimization problem and a constraints satisfaction problem. The second part of this report handles large neighborhood search. We present a new operator of neighborhood, who determines by an algorithm of dynamic programming the optimal sub-sequence of a road in a graph. We show that this operator is quite particularly intended for problems of tours for which all the vertices do not require to be visited. We call this class of problem the Problems of Tours with Partial Cover and present some problems being a part of this class. Chapters 3 and 4 show, through consequent experimental tests, the efficiency of the operator which we propose by applying this search to wide neighborhood on two problems, respectively the Traveling Purchaser Problem (TPP) and Generalized Traveling Salesman Problem ( GTSP). We show while this operator can be combined in a effective way with classic metaheuristics, such as genetic algorithms or algorithms of Ant Colony OptimizationAVIGNON-Bib. numĂ©rique (840079901) / SudocSudocFranceF
Dynamic Cooperative Search for constraint satisfaction and combinatorial optimization : application to a rostering problem
International audienceIn this paper, we are interested in enumerative resolution methods for combinatorial optimization (COP) and constraint satisfaction problems (CSP). We introduce a new approch for the management of branching, called Dynamic Cooperative Search (DCS) inspired from the impact-based search method proposed in Refalo (2004) for CSPs. This method defines in a dynamic way priority rules for variable and value selection in the branching scheme. These rules are meant to be independent of the considered problem. As in Refalo (2004), the principle is to take into account, through learning methods, of the impact of branching decisions in already explored subparts of the search tree. We show the interest of DCS on a real-life rostering problem
Memetic Algorithms for Business Analytics and Data Science: A Brief Survey
This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018