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FFbat: A security and cost-aware workflow scheduling approach combining firefly and bat algorithms

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第24期29卷 e4295.1-e4295.13页

作者： Arunarani, A. R. Manjula, D. Sugumaran, Vijayan Anna Univ India Dept Comp Sci & Engn Madras Tamil Nadu India Oakland Univ Dept Decis & Informat Sci Rochester MI 48063 USA

Cloud computing is distributed computing on a large scale driven by practical and effective operations, in which a pay-per-use framework provides dynamic scaling in response to the needs of workflow applications. Many existing cloud computing environments do not effectively employ security measures to counter security threats in task scheduling. To improve the scheduling system, we include security service to the scheduling process. However, adding security services to applications inevitably causes overhead in terms of computation time. The tradeoff between achieving high computing performance and providing the desired level of security protection imposes a big challenge for task scheduling. To solve this problem, we propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. Our proposed algorithm is based on the hybrid optimization approach, which combines Firefly and bat algorithms. The coding strategy is to minimize the total execution cost while meeting the deadline and risk rate constraints. The proposed system uses a multi-objective function, and the results indicate that our algorithm always outperforms the traditional algorithms.

关键词： bat algorithm computational cost Firefly algorithm risk rate security workflow scheduling

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Time Series Prediction Using Complex-Valued Legendre Neural Network with Different Activation Functions 14th

Time Series Prediction Using Complex-Valued Legendre Neural ...

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14th International Conference on Intelligent Computing (ICIC)

作者： Yang, Bin Zhang, Wei Wang, Haifeng Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang Peoples R China

ISBN: (纸本)9783319959573;9783319959566

In order to enchance the flexibility and functionality of Legendre neural network (LNN) model, complex-valued Legendre neural network (CVLNN) is proposed to predict time series data. bat algorithm is proposed to optimize the real-valued and complex-valued parameters of CVLNN model. We investigate performance of CVLNN for predicting small-time scale traffic measurements data by using different complex-valued activation functions like Elliot function, Gaussian function, Sigmoid function and Secant function. Results reveal that Elliot function and Sigmoid function predict more accurately and have faster convergence than Gaussian function and Secant function.

关键词： Complex-valued Legendre neural network Activation function bat algorithm

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Parameter Tuning of a PID Controller with Reactive Bio-Inspired for PMSM

Parameter Tuning of a PID Controller with Reactive Bio-Inspi...

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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： Aguilar-Mejia, Omar Minor-Popocalt, Hertwin Tapia-Olvera, Ruben Univ Popular Autonoma Estado Puebla Dept Posgrad Ingn Puebla Mexico Univ Nacl Autonoma Mexico Dept Energia Elect Mexico City 04510 DF Mexico

ISBN: (纸本)9781538659359

In this paper, a speed control of permanent magnet synchronous motor using bat algorithm, whale algorithm and cuckoo search algorithm is presented. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. In addition, gains tuning of PI controller's is optimized using the mentioned three optimization algorithms. In order to validate the effectiveness of the proposed controller, simulation is performed under variations of load condition and different values for set speed of the permanent magnet synchronous motor.

关键词： bat algorithm whale algorithm cuckoo algorithm Permanent Magnet Synchronous Motor PI controller

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An Optimal Scheduling of Smart Home Appliances using Heuristic Techniques with Real-time Coordination 1

An Optimal Scheduling of Smart Home Appliances using Heurist...

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1st IEEE International Conference on Power, Energy and Smart Grid (ICPESG)

作者： Khalid, Muhammad Usman Javaid, Nadeem COMSATS Inst Informat Technol Islamabad 44000 Pakistan

ISBN: (纸本)9781538654828

With the development of smart grids (SG), it has become possible to schedule the smart appliances effectively in smart homes. The energy consumption pattern of smart appliances modified by home energy management system (HEMS) deployed in smart home. In this paper, two metaheuristic hybrid optimization techniques presented to program the schedule of household appliances. The proposed schemes are hybrid of tabu search (TS) and bacterial foraging algorithm (BFA) named as hybrid bacterial foraging tabu search algorithm (HBT) and the hybrid of TS and bat algorithm (BA) named as hybrid bat tabu search algorithm (Hbat). The aim is to reduce expenses of the customer while maintaining user comfort in terms of waiting time based on varying price signal. This paper describes two types of scheduling: first appliance scheduling using metaheuristic optimization techniques and second appliance rescheduling using dynamic programming (DP). Metaheuristic optimization techniques move the appliance from high price peak to low price peak minimizing consumer energy bill and peak to average ratio (PAR). DP used to handle the real-time user interrupts to turn ON any appliance. Appliance rescheduling formulated as knapsack problem, which divides a problem into subproblems to achieve an optimal solution. A coordination concept among appliances has been proposed for real-time appliance rescheduling. The simulation results validated the effectiveness of proposed hybrid techniques.

关键词： Bacterial Foraging algorithm Tabu Search bat algorithm Dynamic Programming Smart Grid

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Smart traffic management to support people with color blindness in a Smart City

Smart traffic management to support people with color blindn...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Ochoa, Alberto Oliva, Diego UACJ Tecnol Ciudad Juarez Mexico CUCEIUdG Guadalajara Jalisco Mexico

ISBN: (纸本)9781538646267

Traffic has a significant impact on the viability and efficiency in cities. Smart traffic management aims at making urban driving more seamless and efficient, through the integration of Internet of things (IoT), with a network of interconnected cars and sensors. This paper present a Hybrid Intelligent Application based on bat algorithm and Data Mining -in the preparation of data associated with the instances-to help people who have difficulties identifying the colors to drive with safety by a correct interpretation of traffic signals. To do this, it classifies of regions of the traffic light by analyzing images acquired with a camera. The classification of the colors (red, yellow and green) that are presented in the traffic light is done by three straight line equations that delimit the RGB space, which are tuned by a bio-inspired algorithm, using for this images that are previously labeled with the color that corresponds. Once the color of the light has been classified, an audio aid is produced indicating red, green or yellow, as appropriate, so that people who have difficulties identifying the colors or people with color blindness, can drive properly. Current results are encouraging since they show significant improvement to support people to drive with safety by a correct interpretation of traffic signals.

关键词： Smart applications in a Smart City Bioinspired algorithm bat algorithm Traffic Light Detection Traffic Management People with Color Blindness Smart City

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Diabetes Classification using Radial Basis Function Network by Combining Cluster Validity Index and bat Optimization with Novel Fitness Function

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2017年第1期10卷 247-265页

作者： Cheruku, Ramalingaswamy Edla, Damodar Reddy Kuppili, Venkatanareshbabu Natl Inst Technol Goa Dept Comp Sci & Engn Ponda Goa India

Diabetes is one of the foremost causes for the increase in mortality among children and adults in recent years. Classification systems are being used by doctors to analyse and diagnose the medical data. Radial basis function neural networks are more attractive for classification of diseases, especially in diabetes classification, because of it's non iterative nature. Radial basis function neural networks are four layer feed forward neural network with input layer, pattern layer, summation layer and the decision layer respectively. The size of the pattern layer increases on par with training data set size. Though various attempts have made to solve this issue by clustering input data using different clustering algorithms like k-means, k-medoids, and SOFM etc. However main difficulty of determining the optimal number of neurons in the pattern layer remain unsolved. In this paper, we present a new model based on cluster validity index with radial basis neural network for classification of diabetic patients data. We employ cluster validity index in class by class fashion for determining the optimal number of neurons in pattern layer. A new convex fitness function has also been designed for bat inspired optimization algorithm to identify the weights between summation layer and pattern layer. The proposed model for radial basis function neural network is tested on Pima Indians Diabetes data set and synthetic data sets. Experimental results proved that our approach performs better in terms of accuracy, sensitivity, specificity, classification time, training time, network complexity and computational time compared to conventional radial basis function neural network. It is also proved that proposed model performs better compared to familiar classifiers namely probabilistic neural network, feed forward neural network, cascade forward network, time delay network, artificial immuine system and GINI classifier.

关键词： Radial Basis Function Networks Classification Medical Diagnosis Diabetes Optimal number of clusters bat algorithm

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Application of Nature Inspired algorithms to Optimize Multi-objective Two-Dimensional Rectangle Packing Problem

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JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT-INNOVATION AND ENTREPRENEURSHIP 2019年第4期4卷

作者： Virk, Amandeep Kaur Singh, Kawaljeet Sri Guru Granth Sahib World Univ Dept Comp Sci Fatehgarh Sahib India Punjabi Univ Univ Comp Ctr Dept Comp Sci Patiala Punjab India

This paper considers two-dimensional non-guillotine rectangular bin packing problem with multiple objectives in which small rectangular parts are to be arranged optimally on a large rectangular sheet. The optimization of rectangular parts is attained with respect to three objectives involving maximization of (1) utilization factor, minimization of (2) due dates of rectangles and (3) number of cuts. Three nature based metaheuristic algorithms - Cuckoo Search, bat algorithm and Flower Pollination algorithm - have been used to solve the multi-objective packing problem. The purpose of this work is to consider multiple industrial objectives for improving the overall production process and to explore the potential of the recent metaheuristic techniques. Benchmark test data compare the performance of recent approaches with the popular approaches and also of the different objectives used. Different performance metrics analyze the behavior/performance of the proposed technique. Experimental results obtained in this work prove the effectiveness of the recent metaheuristic techniques used. Also, it was observed that considering multiple and independent factors as objectives for the production process does not degrade the overall performance and they do not necessarily conflict with each other.

关键词： Rectangle packing multi-objective cuckoo search bat algorithm flower pollination

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Application of improved particle swarm optimization in economic dispatch of power system

Application of improved particle swarm optimization in econo...

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作者： Tchapda, G.Y. Gninkeu Wang, Z. Sun, Y. Department of Electrical and Mining Engineering University of South Africa Florida1710 South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Auckland Park2006 South Africa

ISBN: (纸本)9781538636749

This paper introduces an improved particle swarm optimization to solve economic dispatch problems involving numerous constraints. Depending on the type of generating units, there are optimization constraints and practical operating constraints of generators such as prohibited operating zones and ramp rate limits. The algorithm is a hybrid technique made up of particle swarm optimization and bat algorithm. Particle swarm optimization as the main algorithm integrates bat algorithm in order to boost its velocity and adjust the improved solution. The new technique is firstly tested on five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. The simulation results show that it performs better than both particle swarm and bat technique. © 2017 IEEE.

关键词： bat algorithm Economic dispatch Hybrid technique Particle swarm optimization

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bat optimization based neuron model of stochastic resonance for the enhancement of MR images

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2017年第1期37卷 124-134页

作者： Singh, Munendra Verma, Ashish Sharma, Neeraj Indian Inst Technol BHU Sch Biomed Engn Varanasi Uttar Pradesh India Banaras Hindu Univ Inst Med Sci Dept Radiodiag & Imaging Varanasi Uttar Pradesh India

Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images. (C) 2017 Nalecz Institute of Biocybemetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Neuron model of stochastic resonance Mesial temporal sclerosis Cortical dysplasia Image enhancement bat algorithm MR images

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bat and Hybrid bat Meta-Heuristic for Quality of Service-Based Web Service Selection

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第1期26卷 123-137页

作者： Podili, Prashanth Pattanaik, K. K. Rana, Prashanth Singh Atal Bihari Vajpayee Indian Inst Informat Technol A-117Morena Link Rd Gwalior 474015 MP India Indian Inst Technol Sch Comp Engn Hyderabad Andhra Pradesh India Thapar Inst Engn & Technol Patiala Punjab India

Efficient QoS-based service selection from a pool of functionally substitutable web services (WS) for constructing composite WS is important for an efficient business process. Service composition based on diverse QoS requirements is a multi-objective optimization problem. Meta-heuristic techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and variants of PSO have been extensively used for solving multi-objective optimization problems. The efficiency of any such meta-heuristic techniques lies with their rate of convergence and execution time. This article evaluates the efficiency of bat and Hybrid bat algorithms against the existing GA and Discrete PSO techniques in the context of service selection problems. The proposed algorithms are tested on the QWS data set to select the best fit services in terms of maximum aggregated end-to-end QoS parameters. Hybrid bat is found to be efficient for service composition.

关键词： Web service composition meta-heuristic particle swarm optimization (PSO) bat algorithm Hybrid bat algorithm

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