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2nd International Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Golmaryami, Marjan Behzadi, Marziveh Ahmadzadeh, Marziyeh Shiraz Univ Technol Dept Comp Engn & Informat Technol Shiraz Iran

ISBN: (纸本)9781467365062

Recent developments in the stock market have created an urgent need for efficient methods to help stockholders take appropriate decisions about their stocks. Since large fluctuations occur in the stock market over time and there are many parameters which influence this, it seems difficult to make good decisions that are also well-timed. The purpose of this study is to apply artificial neural networks (ANNs), which can deal with time series data and nonlinear parameters, to predict the next day's stock price. This research has trained the proposed ANN with a meta-heuristic bat algorithm which has a fast and powerful convergence. The recommended method has been applied to stock price forecasting for the first time. This work has used a seven-year dataset of a private bank stocks in order to prove the performance of the suggested method. After data pre-processing, three types of ANNs (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) were employed to predict the stocks' closing price. Afterwards, MATLAB was used to evaluate the performance of these three methods by scoring the target of the mean absolute percentage error (MADE). This paper indicates that the bat algorithm adjusts the weight matrix of ANN more precisely than the two other algorithms. The results may be adapted to other companies' stocks.

关键词： Artificial Neural Networks Back Propagation algorithm particle swarm optimization algorithm Bat algorithm Stock Market Forecasting

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A Hybrid algorithm for DAG Application Scheduling on Computational Grids 1

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1st International Conference on Mobile, Secure, and Programmable Networking (MSPN)

作者： Bouali, Lyes Oukfif, Karima Bouzefrane, Samia Oulebsir-Boumghar, Fatima UMMTO LARI Lab Tizi Ouzou Algeria UMMTO Compute Sci Dept Tizi Ouzou Algeria USTHB LRPE Lab Bab Ezzouar Algeria CNAM CEDRIC Lab Paris France

ISBN: (数字)9783319257440

ISBN: (纸本)9783319257440;9783319257433

In the late three decades, grid computing has emerged as a new field providing a high computing performance to solve larger scale computational demands. Because Directed Acyclic Graph (DAG) application scheduling in a distributed environment is a NP-Complete problem, meta-heuristics are introduced to solve this issue. In this paper, we propose to hybridize two well-known heuristics. The first one is the Heterogeneous Earliest Finish Time (HEFT) heuristic which determines a static scheduling for a DAG in a heterogeneous environment. The second one is particle swarm optimization (PSO) which is a stochastic meta-heuristic used to solve optimization problems. This hybridization aims to minimize the makespan (i.e., overall completion time) of all the tasks within the DAG. The experimental results that have been conducted under hybridization show that this approach improves the scheduling in terms of completion time compared to existing algorithms such as HEFT.

关键词： Grid computing Task scheduling Directed acyclic graph Heterogeneous earliest finish time algorithm particle swarm optimization algorithm Makespan

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Chaos PSO algorithm Based Economic Dispatch of Hybrid Power Systems Including Solar and Wind Energy Sources

Chaos PSO Algorithm Based Economic Dispatch of Hybrid Power ...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA 2015)

作者： Huynh, Duy C. Nair, Nirmal Ho Chi Minh City Univ Technol Dept Elect Engn Ho Chi Minh City Vietnam Univ Auckland Elect & Comp Engn Dept Auckland New Zealand

ISBN: (纸本)9781509012374

This paper proposes a novel application of a chaos particle swarm optimization (PSO) algorithm for economic dispatch (ED) of a hybrid power system including the solar and wind energy sources. The algorithm is seeking to minimize total operating costs of the hybrid power system. The proposed chaos PSO algorithm is one of the standard PSO algorithm variants which has been used a logistic map for initializing random values of generators, as well as the inertia weight in the velocity update equation of the standard PSO algorithm. This results in the best convergence capability and search performance during the evolution process of the algorithm. The chaos PSO algorithm based ED problem of the hybrid power system with and without solar and wind powers is considered on a standard IEEE 30-bus 6-generator 41-transmission line test power system. The simulation results demonstrate the capabilities of the proposed algorithm to generate optimal dispatch solutions of the ED problem considering the renewable energy resources. The comparison with the standard PSO algorithm demonstrates the superiority of the proposed algorithm and confirms its potential to solve the ED problem.

关键词： Economic dispatch hybrid power systems particle swarm optimization algorithm

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particle swarm optimization Least Square Support Machine Based Missing Data Imputation algorithm in Wireless Sensor Network for Nuclear Power Plant's Environmental Radiation Monitor

Particle Swarm Optimization Least Square Support Machine Bas...

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2nd International Conference on Materials and Products Manufacturing Technology (ICMPMT 2012)

作者： Gao, Song Tang, Yaogeng Qu, Xing South Univ China Inst Elect Heng Yang 421001 Peoples R China

ISBN: (纸本)9783037855447

In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm is proposed based on particle swarm optimized least squares support vector machine. This algorithm imputes missing data utilizing node's previous monitoring data and neighbor node's current monitoring data jointly. Experimental results using the real radiation monitoring data around a nuclear power plant show that the proposed algorithm can impute the missing gamma dose rate data accurately.

关键词： environmental radiation monitoring wireless sensor network missing data imputation least squares support vector machine particle swarm optimization algorithm

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Bio Inspired Approach for Load Balancing to Reduce Energy Consumption in Cloud Data Center

Bio Inspired Approach for Load Balancing to Reduce Energy Co...

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2015 Communication Control and Intelligent Systems (CCIS)

作者： Goyal, Akhil Chahal, Navdeep S. C DAC Software Technol Div Mohali India

ISBN: (纸本)9781467375412

Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.

关键词： Cloud Computing Firefly algorithm particle swarm optimization algorithm Energy Consumption Load Balancing

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Performance evaluation of proposed Differential Evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第1期24卷 175-191页

作者： Chakaravarthy, G. Vijay Marimuthu, S. Sait, A. Naveen Fathima Michael Coll Engn & Technol Dept Mech Engn Madurai 625020 Tamil Nadu India Latha Mathavan Engn Coll Dept Mech Engn Madurai 625301 Tamil Nadu India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai 622507 India

We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. To solve this problem, we propose a Differential Evolution algorithm (DEA) and particle swarm optimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem.

关键词： Flow shop Lot streaming Scheduling Differential evolution algorithm particle swarm optimization algorithm

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Identifying New Prognostic Features for Remaining Useful Life Prediction Using particle Filtering and Neuro-Fuzzy System Predictor 15

Identifying New Prognostic Features for Remaining Useful Lif...

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IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

作者： Boukra, Tahar Univ 20 Aout 1955 Lab Electrotech Skikda LES Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479979929

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the development of degradation model is difficult, we must be addressed to the identification of new features having an obvious trending quality. in this context, This paper present a new selection method based upon a particle swarm optimization algorithm to identify the advanced prognosis feature and a particle filtering for the prediction of the remaining useful life. The fault growth model is integrated to the particle filter using a Neuro-Fuzzy System with its process noise. This method was validated on a set of experimental data collected from bearings run-to-failure tests.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection particle Filter Neuro-Fuzzy System

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Optimal Design of Power System stabilizer in a Multi-machine System Using Evolutionary Computing, A Comparative Study

Optimal Design of Power System stabilizer in a Multi-machine...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Nayak, Niranjan Pradhan, Soumya Ray, Sangram Keshari Rout Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EICE Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EE Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781479974559

When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is because, the PSS is low cost, flexible and easy to implement. In this work the problem is designed as a single objective optimization technique to tune the PSS parameters with Eigenvalue analysis. Here the Invasive Weed optimization algorithm which is found suitable for these types of problems is selected as a tool to find optimal solutions. Simulations are executed on a 4-machine power system for different operating conditions such as heavy load, light load and capacitive load. The results are obtained under different fault conditions with Invasive Weed optimization technique and compared the same with PSO. At last it is observed that the IWO technique performs better in damping overshoot and settling time.

关键词： particle swarm optimization algorithm Invasive weed optimization technique Low frequency oscillation Stability of Power system

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Calibration of a Dual-PTZ-Camera System for Stereo Vision Based on Parallel particle swarm optimization Method

Calibration of a Dual-PTZ-Camera System for Stereo Vision Ba...

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Conference on Image Processing - algorithms and Systems XII

作者： Chang, Yau-Zen Wang, Huai-Ming Lee, Shih-Tseng Wu, Chieh-Tsai Hsu, Ming-Hsi Chang Gung Univ Dept Mech Engn Taoyuan 333 Taiwan Chang Gung Mem Hosp Dept Neurosurg Taoyuan 333 Taiwan

ISBN: (纸本)9780819499363

This work investigates the calibration of a stereo vision system based on two PTZ (Pan-Tilt-Zoom) cameras. As the accuracy of the system depends not only on intrinsic parameters, but also on the geometric relationships between rotation axes of the cameras, the major concern is the development of an effective and systematic way to obtain these relationships. We derived a complete geometric model of the dual-PTZ-camera system and proposed a calibration procedure for the intrinsic and external parameters of the model. The calibration method is based on Zhang's approach using an augmented checkerboard composed of eight small checkerboards, and is formulated as an optimization problem to be solved by an improved particle swarm optimization (PSO) method. Two Sony EVI-D70 PTZ cameras were used for the experiments. The root-mean-square errors (RMSE) of corner distances in the horizontal and vertical direction are 0.192 mm and 0.115 mm, respectively. The RMSE of overlapped points between the small checkerboards is 1.3958 mm.

关键词： PTZ cameras Stereo vision Active camera calibration particle swarm optimization algorithm

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Estimation of Photovoltaic Cells Model Parameters using particle swarm optimization 17th

Estimation of Photovoltaic Cells Model Parameters using Part...

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17th International Workshop on the Physics of Semiconductor Devices (IWPSD)

作者： Khanna, Vandana Das, B. K. Bisht, Dinesh Vandana Singh, P. K. ITM Univ Gurgaon India JIIT Univ Noida India Natl Phys Lab New Delhi India

ISBN: (纸本)9783319030029;9783319030012

swarm intelligence based technique has been used in this work for the estimation of parameters of photovoltaic cells using the two-diode model of the photovoltaic cell. particle swarm optimization algorithm was used to fit the calculated current-voltage characteristics of the photovoltaic cells to the experimental one. The estimated parameters were the generated photocurrent, saturation currents, series resistance, shunt resistance and ideality factors. The proposed approach was validated using industrial photovoltaic cells.

关键词： Photovoltaic Cell particle swarm optimization algorithm Parameter Estimation

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