scholarly journals Quadratization and Roof Duality of Markov Logic Networks

2016 ◽  
Vol 55 ◽  
pp. 685-714
Author(s):  
Roderick Sebastiaan De Nijs ◽  
Christian Landsiedel ◽  
Dirk Wollherr ◽  
Martin Buss
Keyword(s):  
Polynomial Time ◽  
Approximate Solutions ◽  
Computational Time ◽  
Benchmark Problems ◽  
Markov Logic Networks ◽  
Markov Logic ◽  
Maximum Flows

This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers.

scholarly journals A novel method for data conflict resolution using multiple rules

2013 ◽  
Vol 10 (1) ◽  
pp. 215-235 ◽  
Author(s):  
Zhang Yong-Xin ◽  
Li Qing-Zhong ◽  
Peng Zhao-Hui
Keyword(s):  
Conflict Resolution ◽  
Markov Logic Networks ◽  
Crucial Issue ◽  
Real World Data ◽  
Markov Logic ◽  
Single Attribute ◽  
Novel Method ◽  
Integration Data ◽  
Rules Set

In data integration, data conflict resolution is the crucial issue which is closely correlated with the quality of integrated data. Current research focuses on resolving data conflict on single attribute, which does not consider not only the conflict degree of different attributes but also the interrelationship of data conflict resolution on different attributes, and it can reduce the accuracy of resolution results. This paper proposes a novel two-stage data conflict resolution based on Markov Logic Networks. Our approach can divide attributes according to their conflict degree, then resolves data conflicts in the following two steps: (1)For the week conflicting attributes, we exploit a few common rules to resolve data conflicts, such rules as voting and mutual implication between facts. (2)Then, we resolve the strong conflicting attributes based on results from the first step. In this step, additional rules are added in rules set, such rules as inter-dependency between sources and facts, mutual dependency between sources and the influence of week conflicting attributes to strong conflicting attributes. Experimental results using a large number of real-world data collected from two domains show that the proposed approach can significantly improve the accuracy of data conflict resolution.

scholarly journals ON SAMPLING BASED METHODS FOR THE DUBINS TRAVELING SALESMAN PROBLEM WITH NEIGHBORHOODS

2015 ◽  
Vol 2 (2) ◽  
pp. 57-61
Author(s):  
Petr Váňa ◽  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Solution Quality ◽  
Goal Region ◽  
Dubins Vehicle ◽  
Proposed Modification ◽  
Dubins Traveling Salesman Problem

In this paper, we address the problem of path planning to visit a set of regions by Dubins vehicle, which is also known as the Dubins Traveling Salesman Problem Neighborhoods (DTSPN). We propose a modification of the existing sampling-based approach to determine increasing number of samples per goal region and thus improve the solution quality if a more computational time is available. The proposed modification of the sampling-based algorithm has been compared with performance of existing approaches for the DTSPN and results of the quality of the found solutions and the required computational time are presented in the paper.

scholarly journals DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS

Irriga ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 798-817
Author(s):  
Saulo de Tarso Marques Bezerra ◽  
José Eloim Silva de Macêdo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Water Distribution ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computational Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Irrigation Network

DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS     SAULO DE TARSO MARQUES BEZERRA1 E JOSÉ ELOIM SILVA DE MACÊDO2   1 Universidade Federal de Pernambuco, Campus Agreste, Núcleo de Tecnologia, Avenida Campina Grande, S/N, Bairro Nova Caruaru, CEP 55014-900, Caruaru, Pernambuco, Brasil. [email protected]. 2 Centro Universitário Maurício de Nassau, Departamento de Engenharia Civil, BR 104, Km 68, S/N, Bairro Agamenon Magalhães, CEP 55000-000, Caruaru, Pernambuco, Brasil. [email protected].     1 RESUMO   Apresenta-se, neste trabalho, um modelo de otimização para o dimensionamento de sistemas pressurizados de distribuição de água para projetos de irrigação. A metodologia empregada é fundamentada no algoritmo Otimização por Enxame de Partículas (PSO), que é inspirada na dinâmica e comportamento social observados em muitas espécies de pássaros, insetos e cardumes de peixes. O PSO proposto foi aplicado em dois benchmark problems reportados na literatura, que correspondem à Hanoi network e a um sistema de irrigação localizado na Espanha. O dimensionamento resultou, para as mesmas condições de contorno, na solução de ótimo global para a Hanoi network, enquanto a aplicação do PSO na Balerma irrigation network demonstrou que o método proposto foi capaz de encontrar soluções quase ótimas para um sistema de grande porte com um tempo computacional razoável.   Palavras-chave: água, irrigação, análise econômica.     BEZERRA, S. T. M.; MACÊDO, J. E. S. LOOPED WATER DISTRIBUTION NETWORKS DESIGN VIA PARTICLE SWARM OPTIMIZATION ALGORITHM     2 ABSTRACT   This paper presents an optimization model for the design of pressurized water distribution systems for irrigation projects. The methodology is based on the Particle Swarm Optimization algorithm (PSO), which is inspired by the social foraging behavior of some animals such as flocking behavior of birds and the schooling behavior of fish. The proposed PSO has been tested on two benchmark problems reported in the literature, which correspond to the Hanoi network and an irrigation system located in Spain. The design resulted in the global optimum for the Hanoi network, while the application of PSO in Balerma irrigation network demonstrated that the proposed method was able to find almost optimal solutions for a large-scale network with reasonable computational time.   Keywords: water, irrigation, economic analysis. O desempenho do método foi comparado com trabalhos prévios, demonstrando convergência rápida e resultados satisfatórios na busca da solução ótima de um sistema com elevado exigência computacional.

scholarly journals Region Filling with Super Resolution Algorithm

2021 ◽  
Vol 9 (VI) ◽  
pp. 3557-3564
Author(s):  
Darakhshan R. Khan
Keyword(s):  
Patch Size ◽  
Research Work ◽  
Super Resolution ◽  
Input Image ◽  
Computational Time ◽  
Low Resolution ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Region Filling

Region filling which has another name inpainting, is an approach to find the values of missing pixels from data available in the remaining portion of the image. The missing information must be recalculated in a distinctly convincing manner, such that, image look seamless. This research work has built a methodology for completely automating patch priority based region filling process. To reduce the computational time, low resolution image is constructed from input image. Based on texel of an image, patch size is determined. Several low resolution image with missing region filled is generated using region filling algorithm. Pixel information from these low resolution images is consolidated to produce single low resolution region filled image. Finally, super resolution algorithm is applied to enhance the quality of image and regain all specifics of image. This methodology of identifying patch size based on input fed has an advantage over filling algorithms which in true sense automate the process of region filling, to deal with sensitivity in region filling, algorithm different parameter settings are used and functioning with coarse version of image will notably reduce the computational time.

scholarly journals Fast Algorithm for Selective Image Segmentation Model

2018 ◽  
Vol 7 (4.33) ◽  
pp. 41
Author(s):  
Abdul K Jumaat ◽  
Ke Chen
Keyword(s):  
Image Segmentation ◽  
Fast Algorithm ◽  
Visual Quality ◽  
Projection Algorithm ◽  
Computational Time ◽  
Segmentation Result ◽  
Numerical Tests ◽  
Specific Object ◽  
The Common

Selective image segmentation model aims to separate a specific object from its surroundings. To solve the model, the common practice to deal with its non-differentiable term is to approximate the original functional. While this approach yields to successful segmentation result, however the segmentation process can be slow. In this paper, we showed how to solve the model without approximation using Chambolle’s projection algorithm. Numerical tests show that good visual quality of segmentation is obtained in a fast-computational time.  

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

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