An improved algorithm for solving a multi-period facility location problem

An exact algorithm for the multi-period facility location problem is proposed that efficiently integrates mixed-integer and dynamic programming methods. Two simplification procedures are introduced to reduce the size of the general multi-period facility location problem substantially. Because the proposed algorithm utilizes dynamic programming to obtain the optimal sequence over the entire planning horizon, many near-optimal solutions also become available that are extremely useful for postoptimality analysis. The solution method is tested and compared with a well-known procedure on several problems with varying conditions. The comparisons appear very promising, and the required CPU times by the proposed method are substantially reduced.     

Fast parallel Preconditioned Conjugate Gradient algorithms for robot manipulator dynamics simulation

In this paper fast parallel Preconditioned Conjugate Gradient (PCG) algorithms for robot manipulator forward dynamics, or dynamic simulation, problem are presented. By exploiting the inherent structure of the forward dynamics problem, suitable preconditioners are devised to accelerate the iterations. Also, based on the choice of preconditioners, a modified dynamic formulation is used to speedup both serial and parallel computation of each iteration. The implementation of the parallel algorithms on two interconnected processor arrays is discussed and their computation and communication complexities are analyzed. The simulation results for a Puma Arm are presented to illustrate the effectiveness of the proposed preconditioners. With a faster convergence due to preconditioning and a faster computation of iterations due to parallelization, the developed parallel PCG algorithms represent the fastest alternative for parallel computation of the problem withO(n) processors.     

Optimal Development of Agricultural Sectors in the Basin Based on Economic Efficiency and Social Equality

The limitation of freshwater resources and the growing demand for water, make the issue of water resource development planning and water allocation among stakeholders even more important. Ideally, water allocation should be economically efficient and socially equitable. In this study, a water allocation model is presented in an integrated framework that considers the interaction of water supply and demand according to economic and social factors. To achieve this, a reliability-based multi-objective optimization - simulation approach has been employed. The objective functions of the problem are: 1) maximizing GDP from agricultural sectors and 2) maximizing social equality in different provinces of the basin (measured using the Williamson coefficient). The fair development and allocation among the shared provinces in the basin can reduce conflicts in the region. Karkheh basin has been considered as a case study and decision variables of the problem are area under cultivation of agricultural development sectors in different provinces. The results show that, without harming the income of the agricultural sector, the spatial distribution of development projects can be done in such a way that equality (according to income level and the number of people working in each province) is achieved. One of the solutions of Pareto front compared to previous studies shows that, in addition to an increase of about 12% of the objective function 1 (GDP), the value of the objective function 2 (Williamson coefficient) decreased from 1.19 to 0.98. This indicates a decrease in income inequality among the provinces of the basin.

     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network

Engineering with Computers - The advent of new data-mining techniques and, more recently, swarm-based optimization algorithms have antiquated traditional models in the field of energy performance...     

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

The Journal of Supercomputing - During recent years, big data explosion and the increase in main memory capacity, on the one hand, and the need for faster data processing, on the other hand, have...     

A firefly algorithm for power management in wireless sensor networks (WSNs)

The Journal of Supercomputing - In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize...     

Optimal pixel aspect ratio for enhanced 3D TV visualization

In multiview 3D TV, a pair of corresponding pixels in adjacent 2D views contributes to the reconstruction of voxels (3D pixels) in the 3D scene. We analyze this reconstruction process and determine the optimal pixel aspect ratio based on which the estimated object position can be improved given specific imaging or viewing configurations and constraints. By applying mathematical modeling, we deduce the optimal solutions for two general stereo configurations: parallel and with vergence. We theoretically show that for a given total resolution a finer horizontal resolution, compared to the usual uniform pixel distribution, in general, provides a better 3D visual experience for both configurations. The optimal value may vary depending on different configuration parameter values. We validate our theoretical results by conducting subjective studies using a set of simulated non-square discretized red–blue stereo pairs and show that human observers indeed have a better 3D viewing experience with an optimized vs. a non-optimized representation of 3D-models.     

Sentic Computing for social media marketing

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.