Combining Particle Filter and Population-based Metaheuristics for Visual Articulated Motion Tracking

Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called \emph{dynamic optimization problems}. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two population-based metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show that the proposed algorithms increase the performance of standard particle filters

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems

G. Luque, E. Alba, Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems, In Proceedings of Intelligent Networking and Collaborative Systems, pp. 184-191, 2-4 September, 2015, Taipei, Taiwan, IEEE PressThis paper proposes a novel algorithm combining path relinking with a set of cooperating trajectory based parallel algorithms to yield a new metaheuristic of enhanced search features. Algorithms based on the exploration of the neighborhood of a single solution, like simulated annealing (SA), have offered accurate results for a large number of real-world problems in the past. Because of their trajectory based nature, some advanced models such as the cooperative one are competitive in academic problems, but still show many limitations in addressing large scale instances. In addition, the field of parallel models for trajectory methods has not deeply been studied yet (at least in comparison with parallel population based models). In this work, we propose a new hybrid algorithm which improves cooperative single solution techniques by using path relinking, allowing both to reduce the global execution time and to improve the efficacy of the method. We applied here this new model using a large benchmark of instances of two real-world NP-hard problems: DNA fragment assembly and QAP problems, with competitive results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Optimal location of car wreck adjusters

Su origen es difícil de determinar debido a las diversas restauraciones que se han llevado a cabo, los datos más antiguos son del siglo XVIII, aunque su origen es sin duda anterior. Junto a la Capilla se habilitó un hospital y asilo de transeúntes. Presenta planta rectangular, de una sola nave cubierta con una artesa de yeso y el presbiterio con bóveda de arista. Tiene una espadaña con campana y veleta de forja. En la puerta podemos encontrar un azulejo polícromo del siglo XVIII con la imagen de la Virgen de los Remedios. En el Interior, en el altar mayor, observamos una imagen de la Virgen de los Remedios, titular de la Capilla y Patrona del pueblo desde 1964. En el presbiterio, dentro de dos retablos neoclásicos, se encuentran las imágenes de San Isidro Labrador y de la Divina Pastora. Desde comienzos del siglo XXI la capilla ha estado cerrada ya que presentaba un elevado estado de deterioro. Debido a esto las imágenes fueron trasladadas a la Iglesia Parroquial. En el año 2012, dicho edificio fue restaurado quedando nuevamente abierto al público

20 years of Greedy Randomized Adaptive Search Procedures with Path Relinking

This is a comprehensive review of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR) over the past two decades. GRASP with PR has become a widely adopted approach for solving hard optimization problems since its proposal in 1999. The paper covers the historical development of GRASP with PR and its theoretical foundations, as well as recent advances in its implementation and application. The review includes a critical analysis of variants of PR, including memory-based and randomized designs, with a total of ten different implementations. It describes these advanced designs both theoretically and practically on two well-known optimization problems, linear ordering and max-cut. The paper also explores the hybridization of GRASP with PR and other metaheuristics, such as Tabu Search and Scatter Search. Overall, this review provides valuable insights for researchers and practitioners seeking to utilize GRASP with PR for solving optimization problems.Comment: 28 pages, 13 figure