Graph Coloring

Tato práce se zabývá barvením grafů, což je přiřazování barev jednotlivým vrcholům grafu tak, aby žádné dva vrcholy spojené hranou neměly stejnou barvu. Tento problém je velmi výpočetně náročný, protože je NP-úplný. Zároveň je velmi důležitý, protože má řadu praktických aplikací. Zde jsou popsány některé heuristické algoritmy, které se tento problém snaží řešit pomocí postupného zlepšování počátečního řešení při zadaném počtu barev. Tři algoritmy byly implementovány a poté otestovány na různých grafech a porovnány vzhledem k různým kritériím.This thesis is about graph coloring, which is assigning colors to vertices of a graph such that no two vertices, which are linked with an edge, have the same color. This problem is very computational hard, because it's NP-complete. It's also very important, because it has many practical applications. Here are described some of the heuristic algorithms, which try to solve this problem by iteratively improving the initial solution with given number of colors. Three of them have been implemented, tested on different graphs and compared considering several criteria.

A study on exponential-size neighborhoods for the bin packing problem with conflicts

We propose an iterated local search based on several classes of local and large neighborhoods for the bin packing problem with conflicts. This problem, which combines the characteristics of both bin packing and vertex coloring, arises in various application contexts such as logistics and transportation, timetabling, and resource allocation for cloud computing. We introduce $O(1)$ evaluation procedures for classical local-search moves, polynomial variants of ejection chains and assignment neighborhoods, an adaptive set covering-based neighborhood, and finally a controlled use of 0-cost moves to further diversify the search. The overall method produces solutions of good quality on the classical benchmark instances and scales very well with an increase of problem size. Extensive computational experiments are conducted to measure the respective contribution of each proposed neighborhood. In particular, the 0-cost moves and the large neighborhood based on set covering contribute very significantly to the search. Several research perspectives are open in relation to possible hybridizations with other state-of-the-art mathematical programming heuristics for this problem.Comment: 26 pages, 8 figure

Solution Methods for a Scheduling Problem with Incompatibility and Precedence Constraints

Consider a project which consists in a set of operations to be performed, assuming the processing time of each operation is at most one time period. In this project, precedence and incompatibility constraints between operations have to be satisfied. The goal is to assign a time period to each operation while minimizing the duration of the whole project and while taking into account all the constraints. Based on the mixed graph coloring model and on an efficient and quick tabu search algorithm for the usual graph coloring problem, we propose a tabu search algorithm as well as a variable neighborhood search heuristic for the considered scheduling problem. We formulate an integer linear program (useful for the CPLEX solver) as well as a greedy procedure for comparison considerations. Numerical results are reported on instances with up to 500 operations

Two genetic algorithms for the bandwidth multicoloring problem

In this paper the Bandwidth Multicoloring Problem (BMCP) and the Bandwidth Coloring Problem (BCP) are considered. The problems are solved by two genetic algorithms (GAs) which use the integer encoding and standard genetic operators adapted to the problems. In both proposed implementations, all individuals are feasible by default, so search is directed into the promising regions. The first proposed method named GA1 is a constructive metaheuristic that construct solution, while the second named GA2 is an improving metaheuristic used to improve an existing solution. Genetic algorithms are tested on the publicly-available GEOM instances from the literature. Proposed GA1 has achieved a much better solution than the calculated upper bound for a given problem, and GA2 has significantly improved the solutions obtained by GA1. The obtained results are also compared with the results of the existing methods for solving BCP and BMCP

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

A grouping hyper-heuristic framework: application on graph colouring

Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimised. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. In this study, we present a novel generic selection hyper-heuristic framework containing a fixed set of reusable grouping low level heuristics and an unconventional move acceptance mechanism for solving grouping problems. This framework deals with one solution at a time at any given decision point during the search process. Also, a set of high quality solutions, capturing the trade-off between the number of groups and the additional objective for the given grouping problem, is maintained. The move acceptance mechanism embeds a local search approach which is capable of progressing improvements on those trade-off solutions. The performance of different selection hyper-heuristics with various components under the proposed framework is investigated on graph colouring as a representative grouping problem. Then, the top performing hyper-heuristics are applied to a benchmark of examination timetabling instances. The empirical results indicate the effectiveness and generality of the proposed framework enabling grouping hyper-heuristics to achieve high quality solutions in both domains. ©2015 Elsevier Ltd. All rights reserved

Un algorithme constructif efficace pour le problème de coloration de graphe

RÉSUMÉ Le problème de coloration de graphe consiste à assigner à chaque sommet une couleur de sorte que deux sommets adjacents n’aient pas la même couleur tout en utilisant le nombre minimal de couleur. C’est l’un des problèmes les plus étudiés en optimisation combinatoire en raison de ses multiples applications (la planification des horaires, l’allocation des ressources, etc.) et de la complexité de sa résolution. De nombreuses méthodes de résolutions ont été proposées pour résoudre le problème de coloration de graphe. Elles peuvent être réparties en trois catégories : les méthodes exactes dont le temps de calcul croît exponentiellement avec le nombre de sommets du graphe, les méthodes constructives qui donnent rapidement une approximation de la solution optimale du problème, et les métaheuristiques qui fournissent de meilleurs résultats mais au prix d’algorithmes plus complexes et plus gourmands en temps de calcul. Dans le présent mémoire, nous avons conçu un algorithme constructif d’ordre polynomial qui colore le graphe, une couleur à la fois, en priorisant les voisins des voisins des nœuds déjà colorés. Les résultats des tests effectués sur les graphes classiques du banc d’essai démontrent l’efficacité de l’algorithme proposé qui, pour un temps raisonnable, donne des résultats similaires que TABUCOL, la métaheuristique la plus connue.----------ABSTRACT The Vertex Coloring Problem (VCP) requires to assign a color to each vertex in such a way that colors on adjacent vertices are different and the number of colors used is minimized. Due to its numerous practical applications (scheduling, resource allocation, etc.) and computational complexity, the VCP is one of the most studied problems in combinatorial optimization. Several methods have been proposed to solve the VCP. They can be classified in three families: exact approaches whose running time increases exponentially with the size of the graph, greedy algorithms which approximate the optimal solution in a short time and metaheuristic methods which are the best performing algorithms, but are more complex and time consuming. In this work, we design a polynomial incremental algorithm which colors the graph, one class at a time by favouring the neighbours of neighbours of already colored vertices. Computational results on the set of DIMACS benchmark instances demonstrate the efficiency of the proposed algorithm which gives the same results as the popular metaheuristic TABUCOL in reasonable running time