This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, raw materials customers considering several pe...
详细信息
This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, raw materials customers considering several periods, existing inventory and shortage in distribution centers, and transportation cost and time. This problem is formulated as a bi-objective integer nonlinear programming model. The aim of this model is to determine numbers and locations of supply chain elements, their capacity levels, allocation structure, mode of transportation between them, amount of transported products between them, amount of existing inventory, and shortage in distribution centers in each period to minimize the sum of system costs and transportation time in the network. To validate this model and show the applicability of it for small-sized problems, GAMS software is used. Because this given problem is NP-hard, a bee Colony Optimization (BCO) algorithm is proposed to solve medium and large-sized problems. Furthermore, to examine the efficiency of the proposed BCO algorithm, the associated results are compared with the results obtained by the Genetic algorithm (GA). Finally, the conclusion is provided. (C) 2016 Sharif University of Technology. All rights reserved.
Fluid dynamics of water flow through porous metallic media is significant for cooling and heating applications. The prediction of the velocity of fluid flowing inside the porous media could provide useful data for pre...
详细信息
Fluid dynamics of water flow through porous metallic media is significant for cooling and heating applications. The prediction of the velocity of fluid flowing inside the porous media could provide useful data for pressure drop calculations. This prediction is usually performed by a mathematical modeling approach like computational fluid dynamics (CFD). The computational method is a powerful means of precision calculation, although it could take more time and expenditure for more complex geometries or more complex fluid flow regimes. Artificial intelligence (AI) algorithms could learn and map CFD data under several conditions. AI algorithms could continue the prediction of physical data, saving computing time and performance. The present study focuses on CFD modeling of water flow inside a pipe filled with copper porous media. For the first time, the fuzzy bee algorithm (BAFIS) maps the generated CFD data to inlet velocities of 0.5, 0.7, 1.1, and 1.3 m/s. The BAFIS intelligence assessment for accurate convective flow prediction in porous media is not available in the literature. Additionally, the reliability of this method for missing CFD data prediction has not been considered yet. The results showed that the maximum intelligence (regression=0.97) is for a bee number of 140. Using the most intelligent BAFIS, the outlet velocity can be predicted by the artificial intelligence method for further nodes and inlet velocities without CFD modeling. BAFIS could precisely predict the outlet velocity for missing data with an inlet velocity of 0.91 m/s based on previously mapped data. A comparison was made between BAFIS and the fuzzy neural network (ANFIS) for CFD data predictions. The mean-square error and the root-mean-square error of ANFIS were slightly more than BAFIS (i.e., 0.1% and 3%, respectively).
Tato diplomová práce je zaměřena na aplikaci evolučních algoritmů při prokládání dat získaných ultrazvukovým snímání tkáně. Proložená křivka slou...
详细信息
Tato diplomová práce je zaměřena na aplikaci evolučních algoritmů při prokládání dat získaných ultrazvukovým snímání tkáně. Proložená křivka slouží k odhadům perfúzních parametrů, umožňuje odhalit případné patofyziologie ve snímané oblasti. Teoretický úvod je věnován perfúzi a jejím parametrům, kontrastním látkám pro ultrazvukovou aplikaci, snímání ultrazvukovou modalitou, optimalizaci, evolučním algoritmům obecně a dvěma zvoleným evolučním algoritmům – genetický algoritmus a včelí algoritmus. Tyto dva algoritmy byly testovány na zašuměných datech získaných z klinických snímků myší s nádorem. V závěrečné části jsou shrnuty výsledky praktické části a poskytnuty návrhy a doporučení pro další možné zpracování.
Intruders are mischievous individuals who devise all possible methods to compromise the integrity, confidentiality and availability of the electronic information systems through intrusion. Intuitively, intrusion in an...
详细信息
Intruders are mischievous individuals who devise all possible methods to compromise the integrity, confidentiality and availability of the electronic information systems through intrusion. Intuitively, intrusion in an information system is an activity which deliberately violates the security policy of that system. Intrusion Detection System (IDS) therefore, is an attempt aimed at curtailing the excesses of the intruders. Based on their model of application, an IDS is either Misused-based or Anomaly-based. However, while trying to track down penetrations by intruders within a network, several irrelevant and redundant features which have consequential effects on the performance and computational resources, crop up. This has necessitated efforts from concerned people and corporate organizations to deploy means of reducing these negative impacts especially in the anomaly- based IDSs. Past research has shown that bee algorithm (BA) has presented the best features selection techniques for IDS. However, because of the fact that there is no perfect system anywhere, there is still room for improvement on it. Membrane computing, with its distributed parallel computing advantage has allowed the BA to be improved upon thereby bringing forth better solution. Therefore in this paper, we propose a new but robust algorithm called membrane algorithm for solving another NP complete optimization problem using the P-system paradigm. More importantly therefore, this paper presents preliminary results on proposed technique of using Membrane Computing (MC) to enhance the performance of a BA based feature selection of anomaly IDS. The data used for the experiments were randomly taken from Knowledge Discovery and Data mining KDD-Cup 99 dataset. Consequent upon the experiments, our approach produced high Attack Detection Rate (ADR) and significantly reduced False Alarm Rate (FAR).
In this paper, an assignment problem under competence and preference constraints is presented. We are particularly interested simultaneously to ensure a better matching between task requirements and human resources co...
详细信息
In this paper, an assignment problem under competence and preference constraints is presented. We are particularly interested simultaneously to ensure a better matching between task requirements and human resources competences and to satisfy the expressed preferences. For that purpose, we propose a new approach that consists of four steps to solve the assignment problem under competences and preferences constraints. A mathematical formulation and an appropriate solution method are presented. Indeed, a new algorithm based on the hybridization of the bee algorithm and the immune system is developed. Finally, the method is illustrated through a didactic example.
Data imputation is a necessary task to solve missing value problem for better data mining result. The current data imputation with bees algorithm contains several random procedures including instance selection and fea...
详细信息
ISBN:
(纸本)9781450372633
Data imputation is a necessary task to solve missing value problem for better data mining result. The current data imputation with bees algorithm contains several random procedures including instance selection and feature selection, and the randomness causes inconsistency and swinging result in iteration. Thus, this work proposes to solve them by applying a heuristic function in those procedures from using importance score in selecting attribute to handle and probability in selecting correlated value. These calculations provide the bees with a guidance direction; thus, there are less random processes and should lower inconsistent and swinging results from randomness. From evaluation, the proposed bees-based imputation obtained higher accuracy than the previous bees-based and Genetic algorithm-based imputation method from all data sets for all missing data percentage between 10% to 50%. The best improvement in accuracy for 23% in average was found in SPECT data set which consists of only binary type values. For the data sets with values mixing of binary and category type, the proposed method gained about 3-7% improvement in average.
暂无评论