A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

Solving differential equations with Fourier series and Evolution Strategies

A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.     

Porous structures printed by fused deposition of granules processed from a novel polyamide/alumina-based system

Alumina ceramic porous structures were shaped by the fused deposition of alumina/polyamide 612 composite granules using an FDM 3D printer with a modified extruder to process granulated powder instead of a filament. The composite granules were prepared via thermally induced phase separation (TIPS). Firstly, in order to determine the proportion of polymer/solvent to be used in the TIPS process, different volume fractions of PA612 in dimethyl sulfoxide (DMSO) were studied (0.01 to 0.20), and the granules obtained were characterized by SEM, DSC, and Raman spectroscopy. Secondly, the addition of different ceramic loadings to the PA612/DMSO solution was studied with the aim of determining the amount to be used for preparing the composite granules by TIPS. In particular, the effect of the alumina content on the morphology and size of the obtained composite granules was studied. In addition, rheological properties (oscillatory and rotational tests) of the feed materials were studied. Finally, the printing conditions, including the nozzle temperature and nozzle diameter, and the extrusion process, were optimized in order to obtain porous structures with good quality. Alumina porous structures were successfully printed, debinded, and sintered. Adequate bonding between layers was achieved, and no defects at interfaces were detected.     

Participatory Modeling Workshops in a Water-Stressed Basin Result in Gains in Modeling Capacity but Reveal Disparity in Water Resources Management Priorities

Participatory modeling workshops were held in Sonora, México, with the goal of developing water resources management strategies in a water-stressed basin. A model of the water resources system, consisting of watershed hydrology, water resources infrastructure, and groundwater models, was developed deliberatively in the workshops, along with scenarios of future climate and development. Participants used the final version of the water resources systems model to select management strategies. The performance of the strategies was based on the reliability of meeting current and future demands at a daily time scale over a year’s period. Pre- and post-workshop surveys were developed and administered. The survey questions focused on evaluation of participants’ modeling capacity and the utility and accuracy of the models. The selected water resources strategies and the associated, expected reliability varied widely among participants. Most participants could be clustered into three groups with roughly equal numbers of participants that varied in terms of reliance on expanding infrastructure vs. demand modification; expectations of reliability; and perceptions of social, environmental, and economic impacts. The wide range of strategies chosen and associated reliabilities indicate that there is a substantial degree of uncertainty in how future water resources decisions could be made in the region. The pre- and post-survey results indicate that participants believed their modeling abilities increased and beliefs in the utility of models increased as a result of the workshops.     

Effects of physical refining on contents of waxes and fatty alcohols of refined olive oil

Changes in the contents of waxes and fatty alcohols during deodorization/physical refining of bleached olive oil were studied. Experiments were carried out with 1.85% acidity oil, which was physically refined in a discontinuous deodorizer of 250-kg maximum capacity using nitrogen as stripping gas instead of steam. The variables studied were load and temperature of oil in the deodorizer as well as N₂ flow. Analyses of waxes and alcohols were carried out at different operation times. The maximum content of wax was always observed when the oil reached the deodorization temperature. The variation in the wax content depended on temperature and N₂ flow. Wax decomposition started and continued during the operating time, and a progressive decrease, which was pronounced between 3 and 4 h, was observed. Small changes in waxes were observed between 4 and 5 h. Total content of fatty alcohols diminished throughout the operating time, and changes did not depend on the variables studied.     

A complete and efficient CUDA-sharing solution for HPC clusters

In this paper we detail the key features, architectural design, and implementation of rCUDA, an advanced framework to enable remote and transparent GPGPU acceleration in HPC clusters. rCUDA allows decoupling GPUs from nodes, forming pools of shared accelerators, which brings enhanced flexibility to cluster configurations. This opens the door to configurations with fewer accelerators than nodes, as well as permits a single node to exploit the whole set of GPUs installed in the cluster. In our proposal, CUDA applications can seamlessly interact with any GPU in the cluster, independently of its physical location. Thus, GPUs can be either distributed among compute nodes or concentrated in dedicated GPGPU servers, depending on the cluster administrator’s policy. This proposal leads to savings not only in space but also in energy, acquisition, and maintenance costs. The performance evaluation in this paper with a series of benchmarks and a production application clearly demonstrates the viability of this proposal. Concretely, experiments with the matrix–matrix product reveal excellent performance compared with regular executions on the local GPU; on a much more complex application, the GPU-accelerated LAMMPS, we attain up to 11x speedup employing 8 remote accelerators from a single node with respect to a 12-core CPU-only execution. GPGPU service interaction in compute nodes, remote acceleration in dedicated GPGPU servers, and data transfer performance of similar GPU virtualization frameworks are also evaluated.     

Change detection by probabilistic segmentation from monocular view

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA Graphics Processing Unit in order to fulfill real-time execution requirements.     

Background subtraction model based on color and depth cues

Background subtraction consists of segmenting objects in movement in a video captured by a static camera. This is typically performed using color information, but it leads to wrong estimations due to perspective and illumination issues. We show that multimodal approaches based on the integrated use of color and depth cues produce more accurate and robust results than using either data source independently. Depth is less affected by issues such as shadows or foreground objects similar to background. However, objects close to the background may not be detected when using only range information, being color information complementary in those cases. We propose an extension of a well-known background subtraction technique which fuses range and color information, as well as a post-processing mask fusion stage to get the best of each feature. We have evaluated the method proposed using a well-defined dataset and different disparity estimation algorithms, showing the benefits of our method for fusion color and depth cues.     

Calmodulin Supports TRPA1 Channel Association with Opioid Receptors and Glutamate NMDA Receptors in the Nervous Tissue

Transient receptor potential ankyrin member 1 (TRPA1) belongs to the family of thermo TRP cation channels that detect harmful temperatures, acids and numerous chemical pollutants. TRPA1 is expressed in nervous tissue, where it participates in the genesis of nociceptive signals in response to noxious stimuli and mediates mechanical hyperalgesia and allodynia associated with different neuropathies. The glutamate N-methyl-d-aspartate receptor (NMDAR), which plays a relevant role in allodynia to mechanical stimuli, is connected via histidine triad nucleotide-binding protein 1 (HINT1) and type 1 sigma receptor (σ1R) to mu-opioid receptors (MORs), which mediate the most potent pain relief. Notably, neuropathic pain causes a reduction in MOR antinociceptive efficacy, which can be reversed by blocking spinal NMDARs and TRPA1 channels. Thus, we studied whether TRPA1 channels form complexes with MORs and NMDARs that may be implicated in the aforementioned nociceptive signals. Our data suggest that TRPA1 channels functionally associate with MORs, delta opioid receptors and NMDARs in the dorsal root ganglia, the spinal cord and brain areas. These associations were altered in response to pharmacological interventions and the induction of inflammatory and also neuropathic pain. The MOR-TRPA1 and NMDAR-TRPA1 associations do not require HINT1 or σ1R but appear to be mediated by calcium-activated calmodulin. Thus, TRPA1 channels may associate with NMDARs to promote ascending acute and chronic pain signals and to control MOR antinociception.