Advances in Intelligent Systems and Computing 1154

Millie Pant · Tarun Kumar Sharma · Rajeev Arya · B. C. Sahana · Hossein Zolfagharinia   Editors

Soft Computing: Theories and ApplicationsProceedings of SoCTA 2019

Advances in Intelligent Systems and Computing

Volume 1154

Series Editor

Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,Warsaw, Poland

Advisory Editors

Nikhil R. Pal, Indian Statistical Institute, Kolkata, India

Rafael Bello Perez, Faculty of Mathematics, Physics and Computing,Universidad Central de Las Villas, Santa Clara, Cuba

Emilio S. Corchado, University of Salamanca, Salamanca, Spain

Hani Hagras, School of Computer Science and Electronic Engineering,University of Essex, Colchester, UK

László T. Kóczy, Department of Automation, Széchenyi István University,Gyor, Hungary

Vladik Kreinovich, Department of Computer Science, University of Texasat El Paso, El Paso, TX, USA

Chin-Teng Lin, Department of Electrical Engineering, National ChiaoTung University, Hsinchu, Taiwan

Jie Lu, Faculty of Engineering and Information Technology,University of Technology Sydney, Sydney, NSW, Australia

Patricia Melin, Graduate Program of Computer Science, Tijuana Instituteof Technology, Tijuana, Mexico

Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro,Rio de Janeiro, Brazil

Ngoc Thanh Nguyen , Faculty of Computer Science and Management,Wrocław University of Technology, Wrocław, Poland

Jun Wang, Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publicationson theory, applications, and design methods of Intelligent Systems and IntelligentComputing. Virtually all disciplines such as engineering, natural sciences, computerand information science, ICT, economics, business, e-commerce, environment,healthcare, life science are covered. The list of topics spans all the areas of modernintelligent systems and computing such as: computational intelligence, soft comput-ing including neural networks, fuzzy systems, evolutionary computing and the fusionof these paradigms, social intelligence, ambient intelligence, computational neuro-science, artificial life, virtual worlds and society, cognitive science and systems,Perception and Vision, DNA and immune based systems, self-organizing andadaptive systems, e-Learning and teaching, human-centered and human-centriccomputing, recommender systems, intelligent control, robotics and mechatronicsincluding human-machine teaming, knowledge-based paradigms, learning para-digms, machine ethics, intelligent data analysis, knowledge management, intelligentagents, intelligent decision making and support, intelligent network security, trustmanagement, interactive entertainment, Web intelligence and multimedia.

The publications within “Advances in Intelligent Systems and Computing” areprimarily proceedings of important conferences, symposia and congresses. Theycover significant recent developments in the field, both of a foundational andapplicable character. An important characteristic feature of the series is the shortpublication time and world-wide distribution. This permits a rapid and broaddissemination of research results.

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More information about this series at http://www.springer.com/series/11156

Millie Pant • Tarun Kumar Sharma •

Rajeev Arya • B. C. Sahana •

Hossein ZolfaghariniaEditors

Soft Computing: Theoriesand ApplicationsProceedings of SoCTA 2019

123

EditorsMillie PantDepartment of Paper TechnologyIIT RoorkeeRoorkee, India

Tarun Kumar SharmaGraphic Era Hill UniversityDehradun, Uttarakhand, India

Rajeev AryaNIT PatnaPatna, India

B. C. SahanaNIT PatnaPatna, India

Hossein ZolfaghariniaRyerson UniversityToronto, ON, Canada

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-981-15-4031-8 ISBN 978-981-15-4032-5 (eBook)https://doi.org/10.1007/978-981-15-4032-5

© Springer Nature Singapore Pte Ltd. 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,Singapore

Preface

This book focusses on the strides made in the domain of soft computing and itsapplications to address the key issues plaguing the domains of image and signalprocessing, supply chain management, computational Biology and Bioinformatics,human resource management, finance and economics. It includes the immaculateworks presented during the 4th International Conference on Soft Computing:Theories and Applications (SoCTA 2019), organized by the Department ofElectronics and Communication Engineering, National Institute of Patna, Bihar,India from 27th–29th December, 2019. This book stands true to its motive ofencouraging young minds and fresh ideas in the field of soft computing.

Roorkee, India Millie PantDehradun, India Tarun Kumar SharmaPatna, India Rajeev AryaPatna, India B. C. SahanaToronto, Canada Hossein Zolfagharinia

v

Contents

Performance Optimization by MANET AODV-DTNCommunication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Deepak Choudhary and Roop Pahuja

Effectiveness of Whale Optimization Based I+PD Controller for LFCof Plug-in Electric Vehicle Included Multi-area System . . . . . . . . . . . . 11Utkarsh Raj and Ravi Shankar

Dual Band Printed Rectangular Ring-Shaped Monopole Antennafor Wireless Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Chandrakant Jatav and Sudhanshu Verma

Printed U-Shaped Monopole Dual Band Antenna for WirelessApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Vikash Chandra Sharma and Sudhanshu Verma

IoT-Enabled Early Prediction System for Epileptic Seizurein Human Being . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Sayali Shinde and Brijesh Iyer

Effective Author Ranking Using Average of Differenth-Index Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Prabhat Kumar Chandra, Vivekanand Jha, and Kumar Abhishek

A Survey Report on Recent Progresses in Nearest NeighborRealization of Quantum Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Anirban Bhattacharjee, Chandan Bandyopadhyay, Bappaditya Mondal,and Hafizur Rahaman

Annual Rainfall Prediction Using Time Series Forecasting . . . . . . . . . 69Asmita Mahajan, Akanksha Rastogi, and Nonita Sharma

A Novel Approach for Design 7:3 and 5:3 Compressors . . . . . . . . . . . 81Ajay Kumar Kushwaha and Vikas Kumar

vii

High-Accurate, Area-Efficient Approximate Multiplierfor Error-Tolerant Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91M. Parvathi

Hoax and Faux of Information Credibility in Social Networks:Explored, Exemplified and Experimented . . . . . . . . . . . . . . . . . . . . . . 103Ram Chatterjee, Hardeo Kumar Thakur, Ridhi Sethi, and Abhishek Pandey

Minimize Power Ratio (PR) in OFDM Using Tone ReservationMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Yogendra Kumar Upadhyaya and Ajay Kumar Kushwaha

A Single-Phase Multi-level Inverter Using a Lesser Numberof Switching Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Ashutosh Kumar Singh, Ravi Raushan, and Pratyush Gauri

Symmetric Key Generation and Distribution Using Diffie-HellmanAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Kaustubh Purohit, Avanish Kumar, Mayank Upadhyay,and Krishan Kumar

Design of Controllers Using PSO Technique for Second-Order StableProcess with Time Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Satyendra Kumar and Moina Ajmeri

A Green Dynamic Internet of Things (IoT)-Battery Powered ThingsAspect-Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Nitin B. Raut and N. M. Dhanya

An Efficient Layout of Single-Layer Full Adder Using QCA . . . . . . . . 165Nilesh Patidar and Namit Gupta

A Review of mm-Wave Power Amplifiers for Next-Generation5G Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Pradeep Gorre, R. Vignesh, Rajeev Arya, and Sandeep Kumar

Vision-Based Automated Traffic Signaling . . . . . . . . . . . . . . . . . . . . . . 185H. Mallika, Y. S. Vishruth, T. Venkat Sai Krishna, and Sujay Biradar

Performance Comparison of SVM and ANN for Reversible ECGData Hiding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197Siddharth Bhalerao, Irshad Ahmad Ansari, and Anil Kumar

Application of Multi-criteria Decision-Making Methodfor the Evaluation of Tamilnadu Private Bus Companies . . . . . . . . . . 209S. M. Vadivel, A. H. Sequeira, Sunil Kumar Jauhar, R. Baskaran,and S. Robert Rajkumar

viii Contents

CNC Machine Shop Floor Facility Layout Design Using GeneticAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223S. M. Vadivel, A. H. Sequeira, Sunil Kumar Jauhar,K. S. Amirthagadeswarn, and T. Aravind Krishna

Source of Treatment Selection for Different States of Indiaand Performance Analysis Using Machine Learning Algorithmsfor Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235Nitima Malsa, Pooja Singh, Jyoti Gautam, Arpita Srivastava,and Santar Pal Singh

Ant Lion Optimization Technique for Minimizationof Voltage Deviation Through Optimal Placementof Static VAR Compensator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247Stita Pragnya Dash, K. R. Subhashini, and J. K. Satapathy

On Vector Variational Inequalities and Vector OptimizationProblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257B. B. Upadhyay and Priyanka Mishra

Characterizations of the Solution Sets for Constrained PseudolinearSemi-infinite Programming Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 269B. B. Upadhyay and Akriti Srivastava

Novel Chaotic Elephant Herding Optimization for MultilevelThresholding of Color Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Falguni Chakraborty, Provas Kumar Roy, and Debashis Nandi

Forecasting Groundwater Fluctuation from GRACE DataUsing GRNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295Dilip Kumar and Rajib Kumar Bhattacharjya

Android Application for Recognition of Indian Origin AgriculturalProducts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309Snehal P. Tarale and Veena Desai

A Fuzzy Logic Based Approach for Prediction of Squamous CellCarcinoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325Saurabh Jha, Ashok Kumar Mehta, and Chandrashekhar Azad

Investigating Multilevel Hesitated Patterns Using VagueSet Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335Abhishek Dixit, Akhilesh Tiwari, and Rajendra Kumar Gupta

Six Switch Three Phase Five-Level Inverter with Sinusoidal PulseWidth Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347Rajesh Kumar Mahto and Ambarisha Mishra

AI-Enabled Real-Time Sign Language Translator . . . . . . . . . . . . . . . . 357Yash Patil, Sahil Krishnadas, Adya Kastwar, and Sujata Kulkarni

Contents ix

A Comparative Performance of Sorting Algorithms: StatisticalInvestigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367Priyadarshini and Anchala Kumari

Evolutionary Computing for Designing Cryptographic Primitivesfor Block Cipher: Challenges and Opportunities . . . . . . . . . . . . . . . . . 381Pratap Kumar Behera and Sugata Gangopadhyay

A True Event-Based Metaheuristic Algorithm Optimized AGCMechanism for a Multi-area Power System . . . . . . . . . . . . . . . . . . . . . 391Sariki Murali and Ravi Shankar

Wireless Emanation of Braille to Text/Voice and Vice Versa . . . . . . . . 403Aishwarya Korde, Omkar Gaikar, Sonam Nikam, and Smita Rukhande

An Exploratory Analysis Pertaining to Stress Detectionin Adolescents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413Mrinal Pandey, Bharti Jha, and Rahul Thakur

Load Frequency Control of an Interconnected Multi-source PowerSystem Using Quasi-oppositional Harmony Search Algorithm . . . . . . . 423Abhishek Saxena and Ravi Shankar

An Extensive Investigation of Wavelet-based Denoising Techniquesfor Various ECG Signals Utilizing Thresholding Function . . . . . . . . . . 433V. Supraja, P. Nageswara Rao, and M. N. Giriprasad

Effect of Noise on Segmentation Evaluation Parameters . . . . . . . . . . . 443V. Vijaya Kishore and V. Kalpana

A Review Paper on Feature Selection Techniques and ArtificialNeural Networks Architectures Used in Thermography for EarlyStage Detection of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455Kumod Kumar Gupta, Ritu Vijay, and Pallavi Pahadiya

An Artificial Neural Network Model for Estimating the Floodin Tehri Region of Uttarakhand Using Rainfall Data . . . . . . . . . . . . . . 467B. G. Rajeev Gandhi, Dilip Kumar, and Hira Lal Yadav

Advanced Virtual Apparel Try Using Augmented Reality(AVATAR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Sourav Shaw, Swapnali Kadam, Shreya Joshi, and Dhanashree Hadsul

A Novel Fault-Detection Scheme for Nearest-Neighbor-BasedReversible Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Anirban Bhattacharjee, Chandan Bandyopadhyay, Bappaditya Mondal,and Hafizur Rahaman

Automated Railway Gate Control Using Internet of Things . . . . . . . . . 501B. Arunjyothi and B. Harikrishna

x Contents

Simulated Annealing Based Algorithm for Tuning LDA HyperParameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515Nikhlesh Pathik and Pragya Shukla

A Better Group Consensus Ranking via a Min-transitive FuzzyLinear Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523Sukhamay Kundu

A Novel Metaheuristic Approach for Resource Constrained ProjectScheduling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535Bidisha Roy and Asim Kumar Sen

A Novel Approach to Handle Huge Data for Refreshment Anomaliesin Near Real-Time ETL Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 545N. Mohammed Muddasir and K. Raghuveer

Comparison of Photodetection Capability of Spin Coated TiO2 ThinFilm and In2O3 Thin Film Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555Rahul Raman, Amitabha Nath, and Mitra Barun Sarkar

Development of IDS Using Supervised Machine Learning . . . . . . . . . . 565Indrajeet Kumar, Noor Mohd, Chandradeep Bhatt,and Shashi Kumar Sharma

Automated Traffic Light Signal Violation Detection System UsingConvolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579Bhavya Bordia, N. Nishanth, Shaswat Patel, M. Anand Kumar,and Bhawana Rudra

An Enhanced Butterfly Optimization Algorithm for FunctionOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593Sushmita Sharma, Apu Kumar Saha, and Sukanta Nama

Dynamic Analysis of Wind Turbine Drivetrain Under ConstantTorque . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605Rishi Kumar and Sankar Kumar Roy

To Build Scalable and Portable Blockchain Application UsingDocker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619Priyanka Kumar and Maharshi Shah

Text Summarization: An Extractive Approach . . . . . . . . . . . . . . . . . . 629Vishal Soni, Lokesh Kumar, Aman Kumar Singh, and Mukesh Kumar

Clifford+T-based Fault-Tolerant Quantum Implementation of CodeConverter Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639Laxmidhar Biswal, Chandan Bandyopadhyay, and Hafizur Rahaman

Contents xi

Applying Deep Learning for Discovery and Analysis of SoftwareVulnerabilities: A Brief Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649Shashank Kumar Singh and Amrita Chaturvedi

Fuzzy Decision Making System for Better Staff PerformanceAppraisal in Institutional Organization . . . . . . . . . . . . . . . . . . . . . . . . 659Soni Sweta and Ajit Kumar Pandey

A Graph-Theoretic Approach for Sustainable New ProductDevelopment (SNPD) in Supply Chain Environment . . . . . . . . . . . . . . 671Amit Kumar Sinha and Ankush Anand

Generalization Performance Comparison of Machine Learners forthe Detection of Computer Worms Using Behavioral Features . . . . . . 677Nelson Ochieng, Waweru Mwangi, and Ismail Ateya

Fully Annotated Indian Traffic Signs Database for Recognition . . . . . . 695Banhi Sanyal, R. K. Mohapatra, and Ratnakar Dash

Streamlining Choice of CNNs and Structure Framing of ConvolutionLayer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705Sonika Dahiya, Rohit Tyagi, and Nishchal Gaba

SNAP N’ COOK—IoT-Based Recipe Suggestion and Health CareApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719Diksha Mukherjee, Albin Paulson, Shajo Varghese, and Mukta Nivelkar

Accuracy-Based Performance Analysis of Alzheimer’s DiseaseClassification Using Deep Convolution Neural Network . . . . . . . . . . . . 731Ketki C. Pathak and Swathi S. Kundaram

Multiple Information Fusion and Encryption Using DWTand Yang-Gu Mixture Amplitude-Phase Retrieval Algorithmin Fractional Fourier Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745Muhammad Rafiq Abuturab

Development of Intrusion Detection System Using Deep Learningfor Classifying Attacks in Power Systems . . . . . . . . . . . . . . . . . . . . . . . 755Ankitdeshpandey and R. Karthi

An Improved Adaptive Transfer Function for Explosion SparkGeneration in Fireworks Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 767Tapas Si and Amit Mukhopadhyay

NSE Stock Prediction: The Deep Learning Way . . . . . . . . . . . . . . . . . 783Ankit K. Barai, Pooja Jain, and Tapan Kumar

Recent Development of AI and IoT in the field of AgricultureIndustries: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793Amith A. Kulkarni, P. Dhanush, B. S. Chethan, C. S. Thammegowda,and Prashant Kumar Shrivastava

xii Contents

Optimized Fuzzy Rule-Based System to Measure Uncertaintyin Human Decision Making System . . . . . . . . . . . . . . . . . . . . . . . . . . . 799Soni Sweta and Kanhaiya Lal

A Review on Detection of Breast Cancer Cells by Using VariousTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813Vanaja Kandubothula, Rajyalakshmi Uppada, and Durgesh Nandan

Analysis of Security Issues and Possible Solutions in the Internetof Things for Home Automation System . . . . . . . . . . . . . . . . . . . . . . . 825P. Sai Ramya and Durgesh Nandan

Utilization of the Internet of Things in Agriculture: Possibilitiesand Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837P. Mani Sai Jyothi and Durgesh Nandan

Study on Real-Time Face Recognition and Tracking for CriminalRevealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849A. Krishna Chaitanya, C. H. Kartheek, and Durgesh Nandan

Analysis of Precision Agriculture Technique by Using MachineLearning and IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859Y. Sasi Supritha Devi, T. Kesava Durga Prasad, Krishna Saladi,and Durgesh Nandan

Dispersive Nature of the FEL Amplifiers in the Whistler Mode . . . . . . 869Ram Gopal, M. Sunder Rajan, Priti Sharma, and Abhinav K. Gautam

An Improved Energy-Efficient Faulty Information ExtractionScheme Using PFDIAES and PFDIF Algorithms . . . . . . . . . . . . . . . . . 883P. T. Kalaivaani and Raja Krishnamoorthy

Cyber Attacks and Security—A Critical Survey . . . . . . . . . . . . . . . . . 895Nithin Kashyap, Hari Raksha K. Malali, and H. L. Gururaj

A Comparative Study on Different Techniques of SentimentalAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905K. S. Peeyusha, G. Pooja, S. Shreyas, and S. P. Pavankumar

An Approach to Select the Proper Combination within Positionaland Non-positional Average Values of Features in ProteinClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913Suprativ Saha and Tanmay Bhattacharya

Areca Nut Disease Detection Using Image Processing . . . . . . . . . . . . . 925A. B. Rajendra, N. Rajkumar, and P. D. Shetty

Simulink Simulation for Predicting Thermodynamic Propertiesof Water–Lithium Bromide Solution Using ANN . . . . . . . . . . . . . . . . . 933Dheerendra Vikram Singh and Tikendra Nath Verma

Contents xiii

A New Bit Plane Specific Longest Repeating Pattern Testfor Statistical Analysis of Bit Sequences . . . . . . . . . . . . . . . . . . . . . . . . 943Bharat Lal Jangid and Ram Ratan

Intelligent Interference Minimization Algorithm for OptimalPlacement of Sensors using BBO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 955Chandra Naik and D. Pushparaj Shetty

Classification of SOA-Based Cloud Services Using Data MiningTechnique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971Zeenat Parween and R. B. S. Yadav

A Novel Clustering-Based Gene Expression Pattern Analysisfor Human Diabetes Patients Using Intuitionistic Fuzzy Setand Multigranulation Rough Set Model . . . . . . . . . . . . . . . . . . . . . . . . 979Swarup Kr Ghosh and Anupam Ghosh

Investigation on HRV Signal Dynamics for MeditativeIntervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993Dipen Deka and Bhabesh Deka

A Review on Deep Learning-Based Channel Estimation Scheme . . . . . 1007Amish Ranjan, Abhinav Kumar Singh, and B. C. Sahana

Patient Diabetes Forecasting Based on Machine LearningApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017Arvind Kumar Shukla

Pose Invariant Face Recognition Using Principal ComponentAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029Akash Krishna Srivastava, H. Sneha, Diksha,and Koushlendra Kumar Singh

An Autonomic Resource Allocation Framework for Service-BasedCloud Applications: A Proactive Approach . . . . . . . . . . . . . . . . . . . . . 1045Tushar Bhardwaj, Himanshu Upadhyay, and Subhash Chander Sharma

Index Point Detection and Semantic Indexing of Videos—AComparative Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059Mehul Mahrishi and Sudha Morwal

Classification of Neuromuscular Disorders Using Machine LearningTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1071Anuj Singh, Arun Vikram, M. P. Singh, and Sudhakar Tripathi

Comparative Study of the Ultrasonic and Infrared PersonCounter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1081Ankit Saxena, Swapnesh Taterh, and Nishant Saxena

xiv Contents

Fuzzy Logic Based Improved Control Design Strategy for MPPT ofSolar PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1093Rahul Bisht, Newton Kumar, and Afzal Sikander

Evaluation of Soil Physical, Chemical Parameter and EnzymeActivities as Indicator of Soil Fertility with SFM Model in IA–AWZone of Rajasthan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107Jyoti Sihag, Divya Prakash, and Parul Yadav

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1123

Contents xv

About the Editors

Dr. Millie Pant is an Associate Professor, Department of Applied Science andEngineering, IIT Roorkee, India. She has supervised 16 Ph.D.s in the area ofnumerical optimization, operations research, soft computing and swarm intelligencetechniques with applications to various engineering design problems, image pro-cessing, computer vision, supply chain management. She has 181 research publi-cations to her credit. She is PI and Co-PI in 2 DST and MHRD sponsored projects.She has edited 7 volumes of Conference Proceedings AISC series of Springer ofSoCPros Conference series since 2017 and 2 volumes of Conference ProceedingsAISC series of Springer of SoCTA Conference series. She is an Associate Editor inInternational Journal of Swarm Intelligence, Inderscience, and Guest Editor inseveral international journals like International Journal of Memetic Computing,Springer, International Journal of Collaborative Engineering, Inderscience. She is areviewer in IEEE Transactions on Evolutionary Computation, Applied SoftComputing, Applied Mathematics and Computation, Neural Network Works,Information science. She has acted as General Chair, Program Chair, Session andTrack Chair in many national and international conferences. She has delivered GuestLectures in the field of swarm intelligence, nature inspired computing, computa-tional intelligence in institution of national and international repute like NationalUniversity of Singapore, Singapore, Liverpool Hope University, Liverpool, UK,Brisbane, Australia, Zakopane, Poland, Graphic Era University, Dehradun, MeerutInstitute of Engineering and Technology, ABV-IIITM Gwalior, MANIT, Bhopal,National Institute of Hydrology, Roorkee, NIT, Assam. She has international col-laboration with MIRS Lab, USA, Liverpool Hope University, UK, and UniversitéParis-Est Créteil Val-de-Marne, Paris, France. She is a founder member of SoCPros,SoCTA, and RAACE Conference series.

Dr. Tarun Kumar Sharma is Professor at Graphic Era Hill University, Dehradun.He worked at Amity School of Engineering and Technology, Amity UniversityRajasthan, India. He holds Ph.D. in Soft Computing from India Institute ofTechnology, Roorkee. He has supervised 2 Ph.D.s. He has 77 research publicationsto his credit. He is one of the founding member of SoCTA series. He acted as

xvii

General Chair of RAACE 2017, Program Chair in SoCTA 2016, OrganizingSecretary in SoCPros 2015, and PC member in SoCPros 2014 and SoCPRos 2013.He has edited 3 volumes of Conference Proceedings published by AISC series ofSpringer Publication and 2 edited books in Asset Analytics, Springer. He is GuestEditor in several international journals like IJACI, IJAIP, IJSIR, IJAMC, IJAEC,IJISDC, and IJPD. He is a member in the editorial board of national and internationaljournals. He has acted as session and track chair in many national and internationalconferences. He has delivered Guest Lectures in the field of swarm intelligence,nature inspired computing, and computational intelligence in the institution ofnational and international repute. He is a founder member of SoCTA and RAACEConference series. He is a member of IET, IANEG, CSTA, and MIRS Lab.

Dr. Rajeev Arya received the EngineeringDegree in Electronics&CommunicationEngineering from Government Engineering College, Ujjain, (RGPV University,Bhopal) India, and the Master of Technology in Electronics & CommunicationEngineering from Indian Institute of Technology (ISM), Dhanbad, India. He receivedthe Ph.D. degree inCommunication Engineering from Indian Institute of Technology,Roorkee, India. He is currently an Assistant Professor with the Department ofElectronics & Communication Engineering at National Institute of Technology,Patna, India. His current research interests are in wireless communication, ant colonyoptimization & soft computing techniques, cognitive radio, signal processing, com-munication systems& circuits design. He has publishedmany articles in internationaljournals and conferences. He is amember of the ISRD and the IAENG.He is an activereviewer in many reputed international journals.

Dr. B. C. Sahana is an Assistant Professor, Department of Electronics andCommunication Engineering, NIT Patna, India. He has supervised 10 M.Tech. and32 B.Tech. students in the area of signal processing, optimization, soft computingand swarm intelligence techniques with applications to various engineering designproblems, image processing and compression, computer vision, geophysical signalprocessing, filter design. He has 21 research publications in journals and confer-ences. He is a reviewer in IEEE access. He has delivered Guest Lectures in the fieldof geophysical signal processing, nature inspired computing, computational intel-ligence in the institution of national and international repute.

Dr. Hossein Zolfagharinia is an award-winning Assistant Professor of OperationsManagement in the Global Management Studies department at the Ted RogersSchool of Management, Ryerson University. He received his Undergraduate andMaster’s Degrees in Industrial Engineering. Following this, he earned his Ph.D. inOperations and Supply Chain Management from the Lazaridis School of Businessand Economics at Wilfrid Laurier University. In addition to his academic back-ground, he has more than four years of professional work experience as a businessmethod analyst in the oil and drilling industry. Furthermore, he gained experience inthe area of Supply Chain Management through managing several projects intrucking and maritime transportation companies in the past (e.g., Logikor Inc.,

xviii About the Editors

Global Spatial Technology Solutions (GSTS) Inc.). Dr. Zolfagharinia’s two mainresearch interests are in: (1) investigating the benefits of collaboration in supplychain management with a focus on logistics and transportation, (2) applying oper-ations management techniques in a healthcare context. He is the author of a bookchapter and several top-tier refereed journal articles. These journals include:European Journal of Operational Research, Transportation Research: Part B andPart E, International Journal of Production Economics, and Operations Research forHealth Care. He frequently presents his work at scholarly/professional conferencesat both the national and international levels (e.g., Institute for Operations Researchand Management Sciences (INFORMS), Annual meeting of Canadian OperationalResearch Society (CORS), and Decision Sciences Institute (DSI)). He is the recipientof the 2017-2018 Dean’s Scholarly, Research, and Creative Activity Award. He hasreceived over $250,000 in the form of research grants, scholarships, and fellowships.These include Natural Sciences and Engineering Research Council of Canada(NSERC), Social Sciences and Humanities Research Council-Institutional Grant(SSHRC-SIG), Ontario Centers of Excellence (OCE), Ontario Graduate Scholarship(OGS), the Best OGS Research Proposal, and the Ontario Institute of the PurchasingManagement Association of Canada (OIPMAC) Achievement Excellence, and he isthe provisional winner of Supply Chain & Logistics Association of Canada PaperCompetition.

About the Editors xix

Performance Optimization by MANETAODV-DTN Communication

Deepak Choudhary and Roop Pahuja

Abstract Mobile Ad Hoc Network (MANET) has the ability to self-configure andestablish a mobile wireless mesh that can be used in extreme conditions, such as inareas affected by disasters. One of the routings in MANET is AODV routing. AODVis one of the reactive routing needed to send data. However, in the implementationof disaster conditions, AODV has weaknesses that are vulnerable to extreme envi-ronmental conditions. In this study, communication will be modeled that leads todisruption due to disaster. MANET AODV-DTN is used to improve network per-formance. With this system, the Probability Delivery Ratio (PDR) parameter valuecan be increased as evidenced by the variable modification of the number of nodesto be 0.431%, reducing the average delay by 63.525%, and producing the energyconsumption increased by 0.170%. Simulation with the variable modification ofspeed obtained by PDR 0.482%, reducing the average delay by 78.710% and energyconsumption increased by 0.167%. Modification of buffer size variables obtained0.729% PDR results, reducing the average delay of 71.603% and energy consump-tion increased by 0.161%. From these data, MANET AODV-DTN is better thanMANET AODV.

Keywords MANET · AODV · DTN · PDR · Average delay

1 Introduction

Condition in the disaster area will affect the rescue process, therefore communicationnetworks are needed that can survive in these conditions. The design and use ofcommunication network systems for disaster areasmust have goodQuality of Servicevalues to ensure that data transmission can reach the destination quickly under limitedenergy. This causes the system performance to be optimal in these conditions.

D. Choudhary (B) · R. PahujaInstrumentation and Control Department, Dr. B.R Ambedkar National Institute ofEngineering–Jalandhar, Jalandhar, Punjab, Indiae-mail: engg_deepak@yahoo.com

© Springer Nature Singapore Pte Ltd. 2020M. Pant et al. (eds.), Soft Computing: Theories and Applications,Advances in Intelligent Systems and Computing 1154,https://doi.org/10.1007/978-981-15-4032-5_1

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2 D. Choudhary and R. Pahuja

Based on [1, 2], the requirement to design a communication network for usein an emergency is higher redundancy when data sent from source to destination.This high redundancy causes data to be rearranged if the message is damage beforereaching the destination, data access is sent quickly, has the capacity to work in emer-gency conditions and normal conditions. Therefore, in this study, MANET networkcommunication will be designed using AODV-DTN and comparing with MANETAODV.

This paper aims to find out and compare the performance of MANET AODV andMANET AODV-DTN so it can be used in disaster conditions. In this study, threeparameters will be tested are Probability Delivery Ratio (PDR), average delay, andenergy consumption through three variable modifications, which are speed, numberof nodes, and buffer size.

2 Background and Related Network

2.1 Mobile Ad Hoc Networks (MANETs)

Mobile Ad hoc Networks (MANETs) is a network that allows the exchange of infor-mation without using infrastructure networks. MANET has a complex distributedsystem and consists of wireless mobile nodes that can be connected freely anddynamic to the network topology. MANET has a traditional network that can reduceinfrastructure costs and easy to implement.

MANET has several networks for different scenarios, a limited structure onMANET means that each node must be a router and responsible for carrying routingpacket tasks. Because it uses one or more routing protocols in MANET, it requires alarge amount ofmessage storagemedia and energywheremobile devices inMANEThave limited memory as a message storage medium [3, 4].

2.2 Ad Hoc on Demand Distance Vector (AODV) [5]

AODV is a reactive routing protocol that starts working when requests from thesource node and find the path will be used to send messages to the destination node.To find the best route, AODV will find Route (Fig. 1) distributing Route Request(RREQ) to all nodes adjacent to source node. At the same time, the broadcast ID andsequence number are sent to avoid sending the same message to a node.

The neighbor node will send RREQ to the next neighbor node until it ends at thedestination node. After RREQ reaches the destination node, the node will reply tothe RREQmessage with Route Reply (RREP). Selected the path is the shortest routewith the lowest cost compared to other routes.

Performance Optimization by MANET AODV-DTN Communication 3

Fig. 1 Example of a figurecaption [6]

To avoid changing network topology, AODV will send HELLO messages con-tinuously. If during the sending process there is a change in the topology from thesource node to the destination node, the node will send the Error Route (RRER) toits neighboring node to the source node. Each node will get the RRER message andthe source node will route again to find the route to the destination.

2.3 Delay Tolerant Networks (DTN)

Delay Tolerant Networks (DTN) are not always available end-to-end networks thatcause message delays. Even though the network has delays in the network, the DTNcan still function then it can work in extreme areas. DTN works using store andforward methods, it means the data packets passing through intermediate nodes willbe stored first before being forwarded. This will be anticipated if the next node cannotbe reached or die or in other limitations [6].

In DTN, system store and forward processes are performed on an additional layercalled a bundle layer. The layer bundle is an additional layer to modify the datapackage with the facilities provided by DTN, the bundle layer is located below theapplication layer. In the bundle layer, the data from the application layer will bebroken into bundles, the bundle function is to store temporary data (Fig. 2).

3 Simulation Design

Scenarios communication design for disaster area, have no connecting with the inter-net. All devices designed in this study are mobile nodes with the traffic of all nodessending and receiving messages. The design is used to compare MANET usingAODV and MANET AODV-DTN. The comparison is done by testing two systemsbased on the simulation results using Network Simulator 2 which takes into account

4 D. Choudhary and R. Pahuja

Fig. 2 Bundle layer in DTN[7]

Quality of Service in the form of PDR, average delay and energy consumption withchanging several variables, namely the number of nodes, speed and buffer size.

In this study, the parameters of the simulation were fixed and used with the samevalues in different simulations. These parameters can be seen in Table 1.

In Table 2 There are three simulation scenarios that will be used in this study.Scenario 1 modification of variable speed changes is done to test changes in thespeed at each node that is used to determine the duration of contacts that occur ateach node. Scenario 2 is modified to change the number of node variables to testthe effect on the probability of sending to the mobility of each node. Scenario 3 is

Table 1 Design simulationparameters

Parameter Value

Packet size 512 byte

Dimension 750 m × 750 m

Total number of nodes 50

Speed (m/s) 2

Radio trans range (m) 180

Simulation time (s) 150

Antenna model Omnidirectional

Propagation model Free space

Pause time (s) 0

Initial energy 1000

Sleep energy 0.05

Transmit power 1

Receive power 1

Transition power 0.2

Transition time 0.001

Performance Optimization by MANET AODV-DTN Communication 5

Table 2 Design simulationparameters

Parameter Scenario 1 Scenario 2 Scenario 3

Num of node 50 20, 30, 40,50, 60, 70, 80

20, 30, 40,50, 60, 70

Speed (m/s) 2, 4, 6, 8, 10 2 2

Buffer size 50 50 50

modified to change the buffer size to test the effect of the number of queues droppedin the buffer.

4 Result and Analysis

The results of the research based on the design have been done in Sect. 3, this sectionwill discuss the results of the simulation and performance analysis of the two systemsMANET AODV and MANET AODV-DTN.

4.1 1st Scenario

In the 1st Scenario, the simulation is designed based on changes in the speed variablesof the MANET AODV route and the MANET AODV-DTN system routing. Thevariable speed is changed by the number of node variables and buffer size which arethe same values as the initial specifications.

From Fig. 3, it illustrates the probability of success of the two systems designed.MANET AODV-DTN has an average probability of 0.98 while MANET AODVhas an average probability of 0.97. MANET AODV-DTN can increase the successof 0.48% delivery compared to MANET AODV. The routing strategy in MANETAODV-DTN sends more data packets than the MANET AODV package deliverystrategy. The MANET AODV-DTN system makes copies of each packet when the

Fig. 3 Graph of simulationresults changes in speedvariables to PDR

6 D. Choudhary and R. Pahuja

Fig. 4 Simulation resultsgraph variable speed changesto average delay

Fig. 5 Simulation resultsgraph variable speed changesto consumption energy

node speed increases, the packet is not easily lost or damaged so it can reach thedestination. If the probability of successful package delivery is greater than reducesdelay. In MANET AODV-DTN each transmission node will cause increased energyconsumption to network while sending, receiving and storing messages (Figs. 4 and5).

4.2 2nd Scenario

In the 2nd Scenario, the simulation is designed based on changes in speed variablesfrom the MANET routing AODV and MANET AODV-DTN routing systems. Vari-able node speed is changed with variable speed and buffer size are equal in value tothe initial specifications.

The probability of successful delivery (Fig. 6) of MANET AODV-DTN is 0.98andMANETAODV has an average probability of 0.98 as well. Because of increasedtraffic on the network to the number of nodes that meet each other, messages willbe exchanged between nodes. However, the average delay (Fig. 7) time of MANETAODV is 23.2 ms bigger than MANET AODV-DTN is 8.48 ms. MANET AODV-DTN has a storage capacity of 100,000, while MANET AODV does not. Then themessage can make contact between nodes more often until there is no queue and thedelay time can be reduced. While the energy consumption used is smaller MANETAODVon average at 980.90 J compared toMANETAODV-DTN at 982.49 J becauseit requires more energy to send data to the message (Fig. 8).

Performance Optimization by MANET AODV-DTN Communication 7

Fig. 6 Graph of simulationresults changes in Num ofnodes variables to PDR

Fig. 7 Graph of simulationresults changes in Num ofnodes variables to Avg delay

Fig. 8 Graph of simulationresults changes in Num ofnodes variables to energyconsumption

4.3 3rd Scenario

In the 3rd scenario, the simulation is designed based on changes in the buffer sizevariables of the MANET AODV andMANET AODV-DTN. The variable buffer sizeis changed by the variable speed and the number of nodes that are the same as theinitial specifications.

In the 3rd scenario, simulation MANET AODV has a successful packet deliveryprobability of an average of 0.96 and MANET AODV-DTN averaging 0.97. Toincrease holding time, a large buffer capacity is needed, MANET AODV-DTN hassufficient buffer capacity compared to MANET AODV so that the average delaytime is better MANET AODV-DTN 7.62 ms while MANET AODV has an averageof 25.23 ms. Energy consumption produced, MANET AODV-DTN increased by0.161% from MANET AODV (Figs. 9, 10, and 11).

8 D. Choudhary and R. Pahuja

Fig. 9 Graph of simulationresults changes in buffer sizevariables to PDR

Fig. 10 Graph of simulationresults changes in buffer sizevariables to Avg delay

Fig. 11 Graph of simulationresults changes in buffer sizevariables to Avg delay

5 Conclusion

The following can be concluded from the simulation results:

1. The higher number of nodes, the higher the PDR value for MANET AODV andMANETAODV-DTN. In the same condition,MANETAODV-DTNcan increasePDR 0.43% compared to MANET AODV. The more messages received by thedestination node, the smaller average delay MANET AODV-DTN. MANETAODV-DTNcan reduce the averagedelayof 63.5%compared toMANETAODV.However, the energy consumption of MANET AODV-DTN increased by 0.17%from MANET AODV.

2. The higher the node speed, the higher the PDR value for MANET AODV-DTNandMANETAODV. In the same conditions, MANETAODV-DTN can increasethe PDR of 0.48% compared to MANET AODV. The more messages receivedby the destination node, the smaller average delay value, MANET AODV-DTN

Performance Optimization by MANET AODV-DTN Communication 9

reduces the average delay of 78.71% compared toMANETAODV. However, theenergy consumption of MANET AODV-DTN increased by 0.16% compared toMANET AODV.

3. The larger the buffer size, the higher the PDR value for MANET AODV-DTNandMANETAODV. In the same conditions, MANETAODV-DTN can increasethe PDR of 0.72% compared to MANET AODV. The more messages the nodereceives, the smaller average delay value, MANET AODV-DTN can reduce theaverage delay by 71.6% compared to MANET AODV. However, the energyconsumption ofMANETAODV-DTN Increased by 0.16% fromMANETAODV.

4. From three scenarios designed, MANET AODV-DTN is more suitable for com-munication for disaster areas because it has a good PDR value and reduces theaverage delay value even though energy consumption is very limited.

References

1. Premkumar, R.: Wireless networks for disaster relief (2014)2. Kishorbhai, V., Vasantbhai, N.: AON: a survey on emergency communication systems during a

catastrophic disaster (2014)3. https://www.divaportal.org/smash/get/diva2:833565/FULLTEXT01.pdf4. Awerbuch, D., Mishra, D.: Ad hoc On Demand Distance Vector (AODV) routing protocol.

Cs.jhu.edu (2017) [Online]. http://www.cs.jhu.edu/~cs647/aodv.pdf. Accessed 11 Nov 2017Waktu 7:08

5. Performance analysis Aodv (Ad Hoc On Demand Distance Vector) and Dsr (Dynamic SourceRouting) protocol to active attack I Manet (Mobile Ad Hoc Network) in term of network Qos(Quality Of Service). Eproc, 1(1). ISSN: 2355-9365.2014

6. Silaban, R.M., Cahyani, N.D., Suryan, V.: Analisis Perbandingan Performansi Ad Hoc On-Demand Distance Vector (Aodv) Dan Zone Routing Protocol (Zrp) Pada Ad-Hoc HybridJaringan Wireless. Teknik Informatika, Fakultas Teknik Informatika, Universitas Telkom

7. Kharisma, B.: Pengenalan internet berbasis Delay Tolerant Network (DTN) [Blog] (2014)

Effectiveness of Whale OptimizationBased I+PD Controller for LFCof Plug-in Electric Vehicle IncludedMulti-area System

Utkarsh Raj and Ravi Shankar

Abstract This study deals with the load frequency control of multi-area, multi-source system. A re-heat thermal generating unit, a gas unit and a plug-in electricvehicle unit is considered in each area. Some physical constraints like Governor deadband and generation rate constraint non-linearity are examined for the thermal unit.Whale optimization algorithm optimized I+PD controller is employed for the loadfrequency control of the proposed system. Load disturbance of 1% is considered forstudying the system dynamics. To show the superiority of the proposed scheme, itsperformance is compared with the performance of the system under PIDN controller.Also, the system is tested against variable load to check the robustness of the system.

Keywords AGC · EV · I+PD · LFC · PIDN · WOA

1 Introduction

Modern power systems are subdivided into various areas for control purpose whichare themselves interconnected by tie lines. These control areas share power with eachother according to the demand on the power system. Due to this, there is fluctuationin the frequency from the nominal value. Also, since the power transfer between theareas is done through the tie lines, hence there is an oscillation in the tie-line powervalue from the scheduled nominal value. Themajor purpose of load frequency control(LFC) is: (i) To maintain the area frequency fluctuations within the scheduled nom-inal values, (ii) To keep tie-line power flow between the areas within the schedulednominal values [1].

For the better implementation of LFC, extensive research work has been done.Various conventional controllers like Integral (I), proportional-integral (PI), and

U. Raj (B)Motihari College of Engineering, Motihari, Indiae-mail: utkarshraj18@gmail.com

R. ShankarNational Institute of Technology, Patna, Indiae-mail: ravi@nitp.ac.in

© Springer Nature Singapore Pte Ltd. 2020M. Pant et al. (eds.), Soft Computing: Theories and Applications,Advances in Intelligent Systems and Computing 1154,https://doi.org/10.1007/978-981-15-4032-5_2

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12 U. Raj and R. Shankar

proportional-integral-derivative (PID) are employed for LFC [2, 3]. Though vari-ous intelligent control techniques like fuzzy logic control, state feedback control,artificial neural network, etc. Upalanchiwar and Sakhare [4], Pal et al. [5] have pro-posed in recent times, but conventional controllers still remain popular due to theirpractical utilities. To minimize the drawbacks of conventional controller I+PD isbeen used for the LFC of the system.

To achieve the best performance from a conventional controller, its settings needto be properly optimized [6]. Various optimization techniques have been proposedin the literature. Genetic algorithm (GA) is being used for controller optimizationby Shankar et al. [7]. Other optimization techniques used in the literature are FOA[8], DE [9], PSO [10], O-SFLA [11] and QOHS [12]. Whale optimization algorithm(WOA) is a novel modern optimization technique from the family of population-based evolutionary algorithms. It simulates the bubble-hunting strategy of humpbackwhales.

Rest of the article has been subdivided into the following sections: Sect. 2 inves-tigates the proposed system and Sect. 3 briefly discusses the WOA. Section 4 showsthe simulation results while Sect. 4 culminates the present research work.

2 System Modeling

2.1 Proposed System

The linearizedmodel of the two-area, multi-source system is considered in this study.A thermal generating unit and gas generating unit has been considered in each controlarea. Physical constraints like G.D.B. and G.R.C. non-linearity are considered forthe thermal generating units. The value of GDB is taken as 0.05% and that of GRCas 3% per minute. A linearized model of plug-in electric vehicle (PEV) has also beenconsidered in each area. The power demand between the grid and the load is balancedusing PEV, consisting of a battery charger. In this study, the discharging/chargingcapacity of PEV is considered as ±5 kW. The transfer function system model ofthe PEV is taken from Saha and Saikia [13]. The proposed system transfer functionmodel is presented in Fig. 1.

2.2 Proposed Controller

In spite of various drawbacks like slower response speed and poor noise rejec-tion capability, conventional controllers still remain popular due to their practicalamenities. The proposed control scheme consists of a sum of integral controller andproportional-derivative controller (I+PD). Based on practical experience, variousconstraints have been applied to the controller parameters.

Effectiveness of Whale Optimization Based I+PD Controller … 13

EV

EV

Thermal Governor Reheat Turbine

Compressor Discharge SystemFuel SystemGas GovernorValve Position

Power System

Power System

Reheat TurbineThermal Governor

Compressor Discharge System

Fuel SystemGas GovernorValve Position

-

+

+

+

-

-

-

-

-

-

+

+

+

+

++

+

+

-

-

-

+

++

+

+-

-

+

Dead Band

Dead Band

+-

GRC

+-

GRC

+

Controller

Controller

Fig. 1 Transfer function model of proposed power system

0 ≤ Kpi , Kii , Kdi ≤ 1 (1)

where i = 1, 2. The block diagram model for the previously discussed proposedcontroller is shown in Fig. 2.

ACE

Controller Input Ki 1/s

1/s

NKd

Kp

Controller Output

Fig. 2 Block diagram model of the proposed controller