22 research outputs found
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 AndalucÃa Tech
Math Oracles: A New Way of Designing Efficient Self-Adaptive Algorithms
In this paper we present a new general methodology to develop self-adaptive methods at a low computational cost. Instead of going purely ad-hoc we de ne several simple steps to include theoretical models as additional information in our algorithm. Our idea is to incorporate the predictive information (future behavior) provided by well-known mathematical models or other prediction systems (the oracle) to build enhanced methods. We show the main steps which should be considered to include this new kind of information into any algorithm. In addition, we actually test the idea on a speci c algorithm, a genetic algorithm (GA). Experiments show that our proposal is able to obtain similar, or even better results when it is compared to the traditional algorithm. We also show the bene ts in terms of saving time and a lower complexity of parameter settings.Universidad de Málaga. Proyecto roadME (TIN2011-28194
Migrants Selection and Replacement in Distributed Evolutionary Algorithms for Dynamic Optimization
Many distributed systems (task scheduling, moving priorities, changing mobile environments, ...) can be linked as Dynamic Optimization Problems (DOPs), since they require to pursue an optimal value that changes over time. Consequently, we have focused on the utilization of Distributed Genetic Algorithms (dGAs), one of the domains still to be investigated for DOPs. A dGA essentially decentralizes the population in islands which cooperate through migrations of individuals. In this article, we analyze the effect of the migrants selection and replacement on the performance of the dGA for DOPs. Quality and distance based criteria are tested using a comprehensive set of benchmarks. Results show the benefits and drawbacks of each setting in dynamic optimization.Universidad de Málaga. Proyecto roadME (TIN2011-28194). Programa de movilidad de la AUIP
Problem Understanding through Landscape Theory
In order to understand the structure of a problem we need to measure some features of the problem. Some examples of measures suggested in the past are autocorrelation and fitness-distance correlation. Landscape theory, developed in the last years in the field of combinatorial optimization, provides mathematical expressions to efficiently compute statistics on optimization problems. In this paper we discuss how can we use optimización combinatoria in the context of problem understanding and present two software tools that can be used to efficiently compute the mentioned measures.Ministerio de EconomÃa y Competitividad (TIN2011-28194
Solving Multi-Objective Hub Location Problems with Robustness
Hub location problems (HLP) are considered in many logistic, telecommunications, and computer problems, where the design of these networks are optimized based on some objective(s) related to the cost or service. In those cases, direct routing between any origin and destination is not viable due to economic or technological constraints.
From the seminal work of O'Kelly~\cite{OKelly86}, a huge number of works have been published in the literature. Early contributions were focused on analogue facility location problems, considering some assumptions to simplify the network design. Recent works have studied more complex models by incorporating additional real-life features and relaxing some assumptions, although the input parameters are still assumed to be known in most of the HLPs considered in the literature. This assumption is unrealistic in practice, since there is a high uncertainty on relevant parameters of real problems, such as costs, demands, or even distances. Consequently, a decision maker usually prefer several solutions with a low uncertainty in their objectives functions instead of the optimum solution of an assumed deterministic objective function.
In this work we use a three-objective Integer Linear Programming model of the p-hub location problem where the average transportation cost, its variance, and the processing time in the hubs are minimized. The number of variables is where is the number of nodes of the graph. ILP solvers can only solve small instances of the problems and we propose in this work the use of a recent hybrid algorithm combining a heuristic and exact methods: Construct, Merge, Solve, and AdaptUniversidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Anytime Algorithms for Multi-Objective Hub Location Problems
In many logistic, telecommunications and computer networks, direct routing of commodities between any origin and destination is not viable due to economic and technological constraints. Hub locations problems (HLPs) are considered in that cases, where the
design of these networks are optimized based on some objective(s) related on the cost or service.
A huge number of papers have been published since the seminal work of O’Kelly. Early works were focused on analogue facility location problems, considering some assumptions to simplify network design. Recent works have studied more complex models that relax some of these assumptions and incorporate additional real-life features. In most HLPs considered in the literature, the input parameters are assumed to be known and deterministic. However, in practice, this assumption is unrealistic since there is a high uncertainty on relevant parameters, such as costs, demands or even distances. As a result, a decision maker usually prefer several
solutions with a low uncertainty in their objectives functions.
In this work, anytime algorithms are proposed to solve the multi-objective hub location problems with uncertainty. The proposed algorithms can be stopped at any time, yielding a set of efficient solutions (belonging to the Pareto front) that are well spread in the objective space.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Path planning with far-away obstacles detection under uncertainty
[Resumen] Hoy en dÃa se están investigando robots de exploración terrestre y espacial más rápidos en respuesta a la creciente demanda de capacidades de exploración e investigación más rápidas, eficaces y rentables. Para estas plataformas móviles rápidas la identificación y evasión de obstáculos lejanos son crÃticas, ya que su alta velocidad implica la necesidad de tener en cuenta el mayor número posible de obstáculos cercanos y lejanos para el cálculo de la trayectoria global, evitando cualquier posible accidente debido a su velocidad y al tiempo de cálculo de los algoritmos de replanificación. Debido a su distancia, los obstáculos lejanos no se incluyen en los mapas locales, que están limitados por el alcance de las cámaras de profundidad. Por estas razones, este artÃculo propone el uso de técnicas de Inteligencia Artificial para detectarlos a partir de imágenes y estimar sus tamaños y posiciones con un cierto grado de incertidumbre. Los obstáculos detectados se incluirán posteriormente en los mapas globales, corrigiendo la trayectoria global en caso de colisionar con ellos.[Abstract] Nowadays faster terrestrial and space exploration robots are being investigated, in response to the growing demand for faster, more efficient, and cost-effective exploration and research apabilities. For these rapid mobile platforms, the identification and avoidance of far obstacles are critical, since their high speed implies the need to take into account as many near and far obstacles
as possible for the global path computation, avoiding any possible accident due to their speed and the computation time of the replanning algorithms. Due to their distance, the far obstacles are not included within the local maps, which are limited by the range of the depth cameras. For these reasons, this paper proposes the use of Artificial Intelligence techniques to detect them from images and estimate their sizes and positions with a certain degree of uncertainty. The detected obstacles will be later included in the global maps, correcting the global path in case it collides with them
An Efficient QAOA via a Polynomial QPU-Needless Approach.
The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem’s expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentially-increasing memory usage when dealing with large problems requiring more qubits. These limitations make today’s QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some -bounded pseudo-Boolean problems, optimizing the exact problem’s expectation value can be done in polynomial time using a classical computer.This work is partially funded by Universidad de Málaga, Ministerio de Ciencia, Innovación y Universidades del Gobierno de España under grants PID 2020-116727RB-I00 (funded by MCIN/AEI/ 10.13039/501100011033) and PRX21/00669; and TAILOR ICT-48 Net- work (No 952215) funded by EU Horizon 2020 research and innovation programme.
Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Path planning with far-away obstacles detection under uncertainty.
Nowadays faster terrestrial and space exploration robots are being investigated, in response to the growing demand for faster, more efficient, and cost-effective exploration and research capabilities. For these rapid mobile platforms, the identification and avoidance of far obstacles are critical, since their high speed implies the need to take into account as many near and far obstacles as possible for the global path computation, avoiding any possible accident due to their speed and the computation time of the replanning algorithms. Due to their distance, the far obstacles are not included within the local maps, which are limited by the range of the depth cameras. For these reasons, this paper proposes the use of Artificial Intelligence techniques to detect them from images and estimate their sizes and positions with a certain degree of uncertainty. The detected obstacles will be later included in the global maps, correcting the global path in case it collides with them.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
3D characterization of a Boston Ivy double-skin green building facade using a LiDAR system
On the way to more sustainable and resilient urban environments, the incorporation of urban green infrastructure (UGI) systems, such as green roofs and vertical greening systems, must be encouraged. Unfortunately, given their variable nature, these nature-based systems are difficult to geometrically characterize, and therefore there is a lack of 3D objects that adequately reflect their geometry and analytical properties to be used in design processes based on Building Information Modelling (BIM) technologies. This fact can be a disadvantage, during the building's design phase, of UGIs over traditional grey solutions. Areas of knowledge such as precision agriculture, have developed technologies and methodologies that characterize the geometry of vegetation using point cloud capture. The main aim of this research was to create the 3D characterization of an experimental double-skin green facade, using LiDAR technologies. From the results it could be confirmed that the methodology used was precise and robust, enabling the 3D reconstruction of the green facade's outer envelope. Detailed results were that foliage volume differences in height were linked to plant growth, whereas differences in the horizontal distribution of greenery were related to the influence of the local microclimate and specific plant diseases on the south orientation. From this research, along with complementary previous research, it could be concluded that, generally speaking, with vegetation volumes of 0.2 m3/m2, using Boston Ivy (Parthenocissus Tricuspidata) under Mediterranean climate, reductions in external building surface temperatures of around 13 °C can be obtained and used as analytic parameter in a future 3D-BIM-object.The authors at GREiA research group would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. The authors at GRAP research group would like to thank the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-464 48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). This work is partially supported by ICREA under the ICREA Academia programme. The authors also wish to thank Dr. Jaume Arnó for his contribution in the statistical analysis of the results