This paper focuses on the application of a genetic algorithm (GA) in estimating the fate and transport parameters of a reacting solute from the column and batch experiments involving a saturated porous medium. A program is developed using C++ to model the column and batch data using kinetically controlled one- or two-site sorption models including linear and/or nonlinear forms. The objective of the algorithm is to minimize the sum of squared differences between the measured and modeled solute concentration data associated with column effluent (i.e., “breakthrough curves”). The GA is capable of estimating transport and reactions parameters such as forward and reverse reaction rates and parameters of the nonlinear reaction models, from a given set of measured data. Further simulations have been performed to estimate the appropriate configurations of the GA, which assist the method in estimating the fate and transport parameters more efficiently. It is shown that a wide range of the GA parameters can lead to convergence to appropriate estimations. The results obtained from this study show that the capability of GAs to fit the column and batch experiment data is promising. 相似文献
This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.
Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore,
the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems
of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review
of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence
systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation,
portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches
have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior
to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However,
this outperformance is not absolute. 相似文献
Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems. 相似文献
In this paper, a dynamic offer generating unit and cognitive layer are suggested for artificial agents based negotiation systems. For this purpose, first, adaptive time and behavior dependent tactics are developed taking advantages from time continuity and dynamics aspects (features) integrated in their modeling. Then, a negotiation strategy (bilateral over single issue) based on these two tactics is suggested. Second, a cognitive negotiation model for a negotiator agent is developed using Win-Lose and Win-Win orientations which will be formed based on personality factors. Afterwards, an experimental validation is conducted for testing applicability of time dependent tactics, the effect of offering time, and the effect of cognitive orientations (Win-Lose and Win-Win) on final negotiation outcomes. The results prove the applicability of the suggested time and behavior dependent tactics as well as the proposed cognitive negotiation model. 相似文献
Traits, as sets of behaviors, can provide a good mechanism for reusability. However, they are limited in important ways and are not present in widely used programming and modeling languages and hence are not readily available for use by mainstream developers. In this paper, we add UML associations and other modeling concepts to traits and apply them to Java and C++ through model-driven development. We also extend traits with required interfaces so dependencies at the semantics level become part of their usage, rather than simple syntactic capture. All this is accomplished in Umple, a textual modeling language based upon UML that allows adding programming constructs to the model. We applied the work to two case studies. The results show that we can promote traits to the modeling level along with the improvement in flexibility and reusability. 相似文献
We study the problem of maintaining a dynamic ordered tree succinctly under updates of the following form: insertion or deletion of a leaf, insertion of a node on an edge (edge subdivision) or deletion of a node with only one child (the child becomes a child of its former grandparent). We allow satellite data of a fixed size to be associated to the nodes of the tree.We support update operations in constant amortized time and support access to satellite data and basic navigation operations in worst-case constant time; the basic navigation operations include parent, first/last-child, previous/next-child. These operations are moving from a node to its parent, leftmost/rightmost child, and its previous and next child respectively.We demonstrate that to efficiently support more extended operations, such as determining the i-th child of a node, rank of a child among its siblings, or size of the subtree rooted at a node, one requires a restrictive pattern for update strategy, for which we propose the finger-update model. In this model, updates are performed at the location of a finger that is only allowed to crawl on the tree between a child and a parent or between consecutive siblings. Under this model, we describe how the named extended operations are performed in worst-case constant time.Previous work on dynamic succinct trees (Munro et al., 2001 [17]; Raman and Rao, 2003 [19]) is mainly restricted to binary trees and achieves poly-logarithmic (Munro et al., 2001 [17]) or “poly-log-log” (Raman and Rao, 2003 [19]) update time under a more restricted model, where updates are performed in traversals starting at the root and ending at the root and queries can be answered when the traversal is completed. A previous result on ordinal trees achieves only sublinear amortized update time and “poly-log-log” query time (Gupta et al., 2007 [11]). More recently, the update time has been improved to O(logn/loglogn) while queries can be performed in O(logn/loglogn) time (Sadakane and Navarro, 2010 [20]). 相似文献
In a previous paper we presented a way to measure the rheological properties of complex fluids on a microfluidic chip (Guillot
et al., Langmuir 22:6438, 2006). The principle of our method is to use parallel flows between two immiscible fluids as a pressure
sensor. In fact, in a such flow, both fluids flow side by side and the size occupied by each fluid stream depends only on
both flow rates and on both viscosities. We use this property to measure the viscosity of one fluid knowing the viscosity
of the other one, both flow rates and the relative size of both streams in a cross-section. We showed that using a less viscous
fluid as a reference fluid allows to define a mean shear rate with a low standard deviation in the other fluid. This method
allows us to measure the flow curve of a fluid with less than 250 μL of fluid. In this paper we implement this principle in
a fully automated set up which controls the flow rate, analyzes the picture and calculates the mean shear rate and the viscosity
of the studied fluid. We present results obtained for Newtonian fluids and complex fluids using this set up and we compare
our data with cone and plate rheometer measurements. By adding a mixing stage in the fluidic network we show how this set
up can be used to characterize in a continuous way the evolution of the rheological properties as a function of the formulation
composition. We illustrate this by measuring the rheological curve of four formulations of polyethylene oxide solution with
only 1.3 mL of concentrated polyethylene oxide solution. This method could be very useful in screening processes where the
viscosity range and the behavior of the fluid to an applied stress must be evaluated. 相似文献
The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized. 相似文献