Do not be afraid of local minima: affine shaker and particle swarm

Stochastic local search techniques are powerful and flexible methods to optimize difficult functions. While each method is characterized by search trajectories produced through a randomized selection of the next step, a notable difference is caused by the interaction of different searchers, as exemplified by the Particle Swarm methods. In this paper we evaluate two extreme approaches, Particle Swarm Optimization, with interaction between the individual "cognitive" component and the "social" knowledge, and Repeated Affine Shaker, without any interaction between searchers but with an aggressive capability of scouting out local minima. The results, unexpected to the authors, show that Affine Shaker provides remarkably efficient and effective results when compared with PSO, while the advantage of Particle Swarm is visible only for functions with a very regular structure of the local minima leading to the global optimum and only for specific experimental conditions

Internet et intranet à la bibliothèque municipale à vocation régionale de Troyes

This paper presents a memory-based Reactive Affine Shaker (M-RASH) algorithm for global optimization. The Reactive Affine Shaker is an adaptive search algorithm based only on the function values. M-RASH is an extension of RASH in which good starting points to RASH are suggested online by using Bayesian Locally Weighted Regression (B-LWR). Both techniques use the memory about the previous history of the search to guide the future exploration but in very different ways. RASH compiles the previous experience into a local search area where sample points are drawn, while locally-weighted regression saves the entire previous history to be mined extensively when an additional sample point is generated. Because of the high computational cost related to the B-LWR model, it is applied only to evaluate the potential of an initial point for a local search run. The experimental results, focussed onto the case when the dominant computational cost is the evaluation of the target $f$ function, show that M-RASH is indeed capable of leading to good results for a smaller number of function evaluations

Quantification of effect of convergence in porous media flow

An attempt is made in this study to quantify the effect of convergence on macroscopic scale in the case of flow through porous media. Experiments are conducted separately on specially conceived parallel flow permeameter and converging flow permeameter keeping identical inlet and outlet conditions, using eight sizes of coarse granular media and water as the fluid medium. The media is sieved through sieves of different sizes to separate the crushed rock into sizes of 3.25 mm, 4.73 mm, 10.00 mm, 11.64 mm, 13.10 mm, 20.10 mm, 28.90 mm and 39.50 mm and glass spheres of 15.41 mm, 18.03 mm and 28.37 mm. As the effect of convergence is predominant in non- Darcian zones of flow, such as flow near the well, flow through rock fills, filters etc.,, the scope of the present work is restricted to flow regime with Re \u3e 10. (After Kovacs) Forchheimer’s equation ( i = aV + bV2 ) is applied to analyze the experimental data. Equations are derived for Darcy parameter (a) and Non-Darcy parameter (b) of the Forchheimer’s equation for the crushed rock and glass spheres by relating to size of the media (d) in both parallel flow condition and converging flow condition. From the results it is inferred that for a given rate of flow through a known size of aquifer having predetermined grain size, the resistance to flow is higher in the parallel flow compared to similar media conditions in converging flow configurations. A comparison is then made between the coefficients of the equation, computed for parallel and converging configurations of flow. The difference in these values is expressed in terms of a factor called ‘Integrated Convergence factor (Cfi)’. It is concluded that the convergence of stream lines of seepage flow has a clear and profound influence on the relationship between resistance and regime. In order to make the findings reliable and suitable to field applications, the derived expressions are subjected to corrections for porosity effect, wall effect and tortuosity effect. Expressions for integrated convergence factor for crushed rocks and glass spheres are Cfi = 1.095 d - 0.079 and Cfi = 0.802 d - 0.25 respectively

An Investigation of Parallel Post-Laminar Flow through Coarse Granular Porous Media with the Wilkins Equation

Behaviour of flow resistance with velocity is still undefined for post-laminar flow through coarse granular media. This can cause considerable errors during flow measurements in situations like rock fill dams, water filters, pumping wells, oil and gas exploration, and so on. Keeping the non-deviating nature of Wilkins coefficients with the hydraulic radius of media in mind, the present study further explores their behaviour to independently varying media size and porosity, subjected to parallel post-laminar flow through granular media. Furthermore, an attempt is made to simulate the post-laminar flow conditions with the help of a Computational Fluid Dynamic (CFD) Model in ANSYS FLUENT, since conducting large-scale experiments are often costly and time-consuming. The model output and the experimental results are found to be in good agreement. Percentage deviations between the experimental and numerical results are found to be in the considerable range. Furthermore, the simulation results are statistically validated with the experimental results using the standard ‘Z-test’. The output from the model advocates the importance and applicability of CFD modelling in understanding post-laminar flow through granular media

Modification, characterization and investigations of key factors controlling the transport of modified nano zero-valent iron (nZVI) in porous media

<p>Enhancement of nano zero-valent iron (nZVI) stability and transport in the subsurface environment is important for <i>in situ</i> degradation of contaminants. Various biodegradable dispersants (poly (acrylic acid) (PAA), Tween 20 and Reetha Extracts) have been tested to evaluate their effectiveness in this regard. Application of dispersants during the synthesis of nZVI have positively affected the reduction in particle size. The transport capacity in terms of fraction elution at different pore water velocities and collector grain size (filter media) was analyzed using correlation equation for the filtration model by Rajagopalan and Tien (RT model). At a surfactant concentration of 5% for PAA, Tween 20 and Reetha (<i>Sapindus trifoliata</i>) extracts, the lowest particle size and the highest zeta potential achieved are 8.67 nm and −55.29 mV, 75.24 nm and −62.68 mV, 61.6 nm and −37.82 mV, respectively. The trend of colloidal stability by The Derjaguin—Landau—Verwey—Overbeek (DLVO) Theory model for PAA and Reetha applied concentration was 3% > 4% > 5% > 2% > 1% > 0%. For Tween 20, modified nZVI particle shows a higher repulsive force with increasing Tween 20 concentration. Results indicated that some mechanisms such as aggregation, ripening and surface modification with different carrier pore water velocities had a considerable impact on nZVI retention in porous media. The results indicate that natural surfactant like Reetha extracts exhibits an alternative potential capacity for nZVI modification in comparison with synthetic surfactants (PAA and Tween 20).</p

Analytic network process based approach for delineation of groundwater potential zones in Korba district, Central India using remote sensing and GIS

The need for augmentation of groundwater resources is attributed to the rapid urbanization and manifold increase in the population in the Indian context. To circumvent the problem the present study is aimed at delineating the groundwater potential zones (GWPZ’s) using Analytic Network Process (ANP) model integrated with Remote Sensing (RS) and Geographic Information System (GIS) in Korba district, Chhattisgarh, Central India. Key parameters selected to achieve the targeted approach include aquifer characteristics, geomorphological features, soil types, slope, drainage density, elevation, lineament density, land use land cover, depth to groundwater level and rainfall data are selected to prepare the GWPZ maps. The final GWPZ map is classified into four categories, viz., poor, moderate, high and very high. Results obtained using these parameters indicate that the groundwater potential zones can be classified from study areas as high to very high (62.86%), moderate (17.95%) and poor (19.19%) category of the total area. In total, 96% model accuracy measurement has been achieved, which is in agreement with the available well yield data. Hence, an ANP based GIS model may be an effective and reliable method for delineating groundwater potential zones in any region that is hitherto unused to its full potential and this would pave way for sustainable groundwater exploration

Evaluating evolutionary algorithms for simulating catchment response to river discharge

Evolutionary algorithms (EAs) are proficient in solving the controlled, nonlinear multimodal, non-convex problems that limit the use of deterministic approaches. The competencies of EA have been applied in solving various environmental and water resources problems. In this study, the storm water management model (SWMM) was set up to authenticate the capability of the model for simulating catchment response in the upper Damodar River basin. Auto-calibration and validation of SWMM were done for the years 2002–2011 at a daily scale using three EAs: genetic algorithms (GAs), particle swarm optimisation (PSO) and shuffled frog leaping algorithm (SFLA). Statistical parameters like Nash–Sutcliffe effectiveness (NSE), percent bias (PBIAS) and root-mean-squared error–observations standard deviation ratio (RSR) were used to analyse the efficacy of the results. NSE and PBIAS values obtained from GA were superior, with the recorded flow with NSE and PBIAS ranging between 0.63 and 0.69 and between 1.12 and 9.81, respectively, for five discharge locations. The value of RSR was approximately 0 indicating the sensibly exceptional performance of the model. The results obtained from SFLA were robust and superior. Our results showed the prospective use and blending of the hydrodynamic model with EA would aid the decision-makers in analysing the vulnerability in river watersheds. HIGHLIGHTS GA, SFLA and PSO coupled with SWMM to characterise temporal dynamics of river discharge in Upper Damodar River Basin.; GA provided model iteration equivalent to 2,000 and performed robustly with NSE and PBIAS ranging between 0.65 and 0.72 and between 1.51 and 9.51, respectively.; SFLA performed comparatively to GA with a higher convergence speed value, whereas PSO performed satisfactorily.

Landslide Susceptibility Mapping in Darjeeling Himalayas, India

Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes