Development of a decision support system based on neural networks and a genetic algorithm0 تومان
Given ever increasing information volume and complexity of engineering, social and economic systems, it has become more difficult to assess incoming data and manage such systems properly. Currently developed innovative decision support systems (DSS) aim to achieve optimum results while minimizing the risks of serious losses. The purpose of the DSS is to help the decision-maker facing the problem of huge amounts of data and ambiguous reactions of complicated systems depending on external factors. By means of accurate and profound analysis, DSSs are expected to provide the user with precisely forecasted indicators and optimal decisions. In this paper we suggest a new DSS structure which could be used in a wide range of difficult to formalize tasks and achieve a high speed of calculation and decision-making. We examine different approaches to determining the dependence of a target variable on input data and review the most common statistical forecasting methods. The advantages of using neural networks for this purpose are described. We suggest applying interval neural networks for calculations with underdetermined (interval) data, which makes it possible to use our DSS in a wide range of complicated tasks. We developed a corresponding learning algorithm for the interval neural networks. The advantages of using a genetic algorithm (GA) to select the most significant inputs are shown. We justify the use of generalpurpose computing on graphics processing units (GPGPU) to achieve high-speed calculations with the decision support system in question. A functional diagram of the system is presented and described. The results and samples of the DSS application are demonstrated.
Generation Expansion Planning in a Pool Based Electricity Market, using Game Theory and Genetic Algorithm0 تومان
Restructuring has changed purpose of generation expansion planning (GEP) from being cost-minimization to
proft-maximization. In this paper, we introduce a new formulation for objective function of generating companies (GENCOs) GEP problem in pool electricity market which includes the revenues of energy and capacity reserve markets and costs of fuel, investment, O&M, outage and emission tax. Moreover, in order to solve GEP problem with above objective function, an algorithm are introduced that use game theory and genetic algorithm for market modeling and optimization of GENCOs objective functions, respectively. To calculate the generation levels of generating units and long-term market price, we have used the traditional probabilistic production costing (PPC) which is modifed to be used in competitive electricity market.
Genetic Algorithm for optimization of optical systems0 تومان
in this paper we described a blind optimization technique with an in-expensive electronics for optical systems tomaximize the output signal. Deformable mirror is the main optical element used in the system to correct the wavefront andincrease the output signal. The mirror is controlled by genetic algorithms through the computer microphone port and two PCIExpress cards.
Optimal Electric Network Design for a Large Offshore Wind Farm Based on a Modified Genetic Algorithm Approach0 تومان
The increasing development of large-scale offshore wind farms around the world has caused many new technical and economic challenges to emerge. The capital cost of the electrical network that supports a large offshore wind farm constitutes a significant proportion of the total cost of the wind farm. Thus, finding the optimal design of this electrical network is an important task, a task that is addressed in this paper. A cost model has been developed that includes a more realistic treatment of the cost of transformers, transformer substations, and cables. These improvements make this cost model more detailed than others that are currently in use. A novel solution algorithm is used. This algorithm is based on an improved genetic algorithm and includes a specific algorithm that considers different cable cross sections when designing the radial arrays. The proposed approach is tested with a large offshore wind farm; this testing has shown that the proposed algorithm produces valid optimal electrical network designs.
Optimal Unit Sizing of Distributed Energy Resources in MicroGrid Using Genetic Algorithm0 تومان
In this paper, a methodology to perform the optimal unit sizing for Distributed Energy Resources (DRE) in MicroGid (MG), is developed. Based on a genetic algorithm, one optimal sizing method was developed to calculate the optimum system configuration that can achieve the customers required loss of power supply probability (LPSP) with a minimum COE. The proposed method results are validated for single source DG and hybrid DG with results obtained from HOMER for the same systems.
Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm0 تومان
The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor singlebuyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time.
Optimized Fuzzy System Using Genetic Algorithm to Detect Faces in Color Images0 تومان
A human face detection method for color images is presented in this paper. The system is composed of three subsystems: skin color segmentation, lip color segmentation and face blobs selection subsystem. Whole these algorithms are fuzzy rule base ones, which are designed empirically, and then optimized by genetic algorithm. In the first stage, skin color regions are selected in the input image. Within each of the skin area, lip pixels are searched using second subsystem, and applied as a feature to identify face candidates in the skin regions. Utilizing the lip area and position relative to the skin area, and face shape information, the third subsystem is materialized to choose face blobs. To precise evaluation of the proposed system, the false positive and false negative of each subsystem, are reported for the empirically designed system as well as the optimized system. Obtained results show a remarkable decrease in false positive and false negative for optimized algorithms compared to empirically designed ones. Finally, 98% detection rate is achieved using proposed method.
Routing in Dynamic Network using Ants and Genetic Algorithm0 تومان
Routing in dynamic network is a challenging one, because the topology of the network is not fixed. This issue is addressed in this presentation using ant algorithm to explore the network using intelligent packets. The paths generated by ants are given as input to genetic algorithm. The genetic algorithm finds the set of optimal routes. The importance of using ant algorithm is to reduce the size of routing table. The significance of genetic algorithm is based on the principle evolution of routes rather than storing the precomputed routes.
The Use of Genetic Algorithm for Feature Selection in Video Concept Detection0 تومان
Video semantic concept detection is considered as an important research problem by the multimedia industry in recent years. Classification is the most accepted method used for concept detection, where, the output of the classification system is interpreted as semantic concepts. These concepts can be employed for automatic indexing, searching and retrieval of video objects. However, employed features have high dimensions and thus, concept detection with the existing classifiers experiences high computation complexity. In this paper, a new approach is proposed to reduce the classification complexity and the required time for learning and classification by choosing the most important features. For this purpose genetic algorithms are employed as a feature selector. Simulation results illustrate improvements in the behavior of the classifier.
Towards an improved heuristic genetic algorithm for static content delivery in cloud storage0 تومان
A key challenge in computer networking is how to organize network topology effectively among a large number of servers in the cloud storage system. In a cloud environment, the topology, which is different from the underlying topology, may be established in any form at any potential edge peers. The cloud content delivery network (CDN) always faces problems of complex distributed path creation, cache update, load balancing, etc. To address the problem as a static content delivery, we propose an Improved Heuristic Genetic Algorithm for Static Content Delivery in Cloud Storage (IHGA-SCDCS) based on a resource management model and cost model. The static content delivery in cloud storage is abstracted into mathematical model for set solving problem, which is then solved by an improved Genetic Algorithm (GA). Finally, the optimal solution is reduced to an optimal content delivery program. The simulation experiment, based on CloudSim, shows that IHGA-SCDCS can effectively obtain optimal solution while reducing delivery cost.
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