An NP-complete matching problem /

"UILU-ENG 79-1713."

Networks of Polarized Evolutionary Processors as Problem Solvers.

In this paper, we propose a solution to an NP-complete problem, namely the "3-colorability problem", based on a network of polarized processors. Our solution is uniform and time efficient.

A clustering approach for vehicle routing problems with hard time windows

Dissertação para obtenção do Grau de Mestre em Logica Computicional

A new MDA-SOA based framework for intercloud interoperability

Cloud computing has been one of the most important topics in Information Technology which aims to assure scalable and reliable on-demand services over the Internet. The expansion of the application scope of cloud services would require cooperation between clouds from different providers that have heterogeneous functionalities. This collaboration between different cloud vendors can provide better Quality of Services (QoS) at the lower price. However, current cloud systems have been developed without concerns of seamless cloud interconnection, and actually they do not support intercloud interoperability to enable collaboration between cloud service providers. Hence, the PhD work is motivated to address interoperability issue between cloud providers as a challenging research objective. This thesis proposes a new framework which supports inter-cloud interoperability in a heterogeneous computing resource cloud environment with the goal of dispatching the workload to the most effective clouds available at runtime. Analysing different methodologies that have been applied to resolve various problem scenarios related to interoperability lead us to exploit Model Driven Architecture (MDA) and Service Oriented Architecture (SOA) methods as appropriate approaches for our inter-cloud framework. Moreover, since distributing the operations in a cloud-based environment is a nondeterministic polynomial time (NP-complete) problem, a Genetic Algorithm (GA) based job scheduler proposed as a part of interoperability framework, offering workload migration with the best performance at the least cost. A new Agent Based Simulation (ABS) approach is proposed to model the inter-cloud environment with three types of agents: Cloud Subscriber agent, Cloud Provider agent, and Job agent. The ABS model is proposed to evaluate the proposed framework.

How hard is a commercial puzzle: the eternity II challenge

Recently, edge matching puzzles, an NP-complete problem, have received, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.

Edge matching puzzles as hard SAT/CSP benchmarks

Recently, edge matching puzzles, an NP-complete problem, have rececived, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.

Traduction statistique par recherche locale

La traduction statistique vise l’automatisation de la traduction par le biais de modèles statistiques. Dans ce travail, nous relevons un des grands défis du domaine : la recherche (Brown et al., 1993). Les systèmes de traduction statistique de référence, tel Moses (Koehn et al., 2007), effectuent généralement la recherche en explorant l’espace des préfixes par programmation dynamique, une solution coûteuse sur le plan computationnel pour ce problème potentiellement NP-complet (Knight, 1999). Nous postulons qu’une approche par recherche locale (Langlais et al., 2007) peut mener à des solutions tout aussi intéressantes en un temps et un espace mémoire beaucoup moins importants (Russell et Norvig, 2010). De plus, ce type de recherche facilite l’incorporation de modèles globaux qui nécessitent des traductions complètes et permet d’effectuer des modifications sur ces dernières de manière non-continue, deux tâches ardues lors de l’exploration de l’espace des préfixes. Nos expériences nous révèlent que la recherche locale en traduction statistique est une approche viable, s’inscrivant dans l’état de l’art.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Optimal capacitor placement in radial distribution networks

The capacitor placement (replacement) problem for radial distribution networks determines capacitor types, sizes, locations and control schemes. Optimal capacitor placement is a hard combinatorial problem that can be formulated as a mixed integer nonlinear program. Since this is a NP complete problem (Non Polynomial time) the solution approach uses a combinatorial search algorithm. The paper proposes a hybrid method drawn upon the Tabu Search approach, extended with features taken from other combinatorial approaches such as genetic algorithms and simulated annealing, and from practical heuristic approaches. The proposed method has been tested in a range of networks available in the literature with superior results regarding both quality and cost of solutions.

Desenvolvimento de extensões para um sistema de carregamento e descarregamento de navios

One of the reasons that influences the efficience of a port terminal that is especialized in movement of containers is in the process of their loading and unloading. The fundamental idea is to determine a plan, to each port in order to decrease the number of total movement of loading and unloading that are necessary during the ship route to every port so that they save time and consequently reduce costs. The goal of this research was to present improvement in the program, written in language C, used to solve to problem of loading and unloading of ships, a NP-complete problem, through the meta-heuristics Simulated Annealing. With the improvement made, an average of 50% reduction of necessary time was gotten to solve the problem in 45 analised instances and in a specific group of these instances the average reduction reached 77 and in 10 instances there was even an improvement in the solution

On the Vertex Separation of Maximal Outerplanar Graphs

We investigate the NP-complete problem Vertex Separation (VS) on Maximal Outerplanar Graphs (mops). We formulate and prove a “main theorem for mops”, a necessary and sufficient condition for the vertex separation of a mop being k. The main theorem reduces the vertex separation of mops to a special kind of stretchability, one that we call affixability, of submops.