Nature-Inspired Algorithms and Applications

Inbunden, Engelska, 2021

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Beskrivning

NATURE-INSPIRED ALGORITHMS AND APPLICATIONS The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide. Among the subjects of the 12 chapters are: A novel method based on TRIZ to map real-world problems to nature problemsApplications of cuckoo search algorithm for optimization problemsPerformance analysis of nature-inspired algorithms in breast cancer diagnosisNature-inspired computation in data miningHybrid bat-genetic algorithm–based novel optimal wavelet filter for compression of image dataEfficiency of finding best solutions through ant colony optimization techniquesApplications of hybridized algorithms and novel algorithms in the field of machine learning.Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.

Produktinformation

Utgivningsdatum:2021-12-14
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:384
Förlag:John Wiley & Sons Inc
ISBN:9781119681748

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Mer om författaren

S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.Anupriya Jain, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana. Sachin Sharma, PhD is an assistant professor in computer applications at the Manav Rachna International Institute of Research and Studies, Faridabad, India. He has published more than 30 research papers in different areas of technology and has been a part of two patents as well. Dinesh Goyal, PhD is the Director at the Poornima Institute of Engineering and Technology, Jaipur, India. His research interests are related to information & network security, image processing, data analytics, and cloud computing, and has published more than 60 research articles. Sonia Duggal, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana. Seema Sharma is an assistant professor at the Manav Rachna International Institute of Research and Studies, Faridabad, India.

Innehållsförteckning

Preface xv1 Introduction to Nature-Inspired Computing 1N.M. Saravana Kumar, K. Hariprasath, N. Kaviyavarshini and A. Kavinya1.1 Introduction 11.2 Aspiration From Nature 21.3 Working of Nature 31.4 Nature-Inspired Computing 41.4.1 Autonomous Entity 51.5 General Stochastic Process of Nature-Inspired Computation 61.5.1 NIC Categorization 81.5.1.1 Bioinspired Algorithm 91.5.1.2 Swarm Intelligence 101.5.1.3 Physical Algorithms 111.5.1.4 Familiar NIC Algorithms 12References 302 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning 33P. Mary Jeyanthi and A. Mansurali2.1 Introduction of Genetic Algorithm 332.1.1 Background of GA 352.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? 352.1.3 Working Sequence of Genetic Algorithm 352.1.3.1 Population 352.1.3.2 Fitness Among the Individuals 362.1.3.3 Selection of Fitted Individuals 362.1.3.4 Crossover Point 372.1.3.5 Mutation 372.1.4 Application of Machine Learning in GA 382.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem 382.1.4.2 Traveling Salesman Problem 392.1.4.3 Blackjack—A Casino Game 402.1.4.4 Pong Against AI—Evolving Agents (Reinforcement Learning) Using GA 412.1.4.5 SNAKE AI—Game 412.1.4.6 Genetic Algorithm’s Role in Neural Network 422.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 432.1.4.8 Frozen Lake Problem From OpenAI Gym 432.1.4.9 N-Queen Problem 442.1.5 Application of Data Mining in GA 442.1.5.1 Association Rules Generation 442.1.5.2 Pattern Classification With Genetic Algorithm 452.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization 462.1.5.4 Market Basket Analysis 462.1.5.5 Job Scheduling 462.1.5.6 Classification Problem 472.1.5.7 Hybrid Decision Tree—Genetic Algorithm to Data Mining 472.1.5.8 Genetic Algorithm—Optimization of Data Mining in Education 472.1.6 Advantages of Genetic Algorithms 472.1.7 Genetic Algorithms Demerits in the Current Era 482.2 Introduction to Artificial Bear Optimization (ABO) 502.2.1 Bear’s Nasal Cavity 522.2.2 Artificial Bear ABO Gist 542.2.3 Implementation Based on Requirement 582.2.3.1 Market Place 582.2.3.2 Industry-Specific 582.2.3.3 Semi-Structured or Unstructured Data 592.2.4 Merits of ABO 602.3 Performance Evaluation 612.4 What is Next? 62References 633 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique 67K. Sasi Kala Rani and N. Pooranam3.1 Introduction 683.1.1 Example of Optimization Process 693.1.2 Components of Optimization Algorithms 703.1.3 Optimization Techniques Based on Solutions 703.1.3.1 Optimization Techniques Based on Algorithms 723.1.4 Characteristics 733.1.5 Classes of Heuristic Algorithms 743.1.6 Metaheuristic Algorithms 753.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature–Inspired 753.1.6.2 Population-Based vs. Single-Point Search (Trajectory) 753.1.7 Data Processing Flow of ACO 763.2 A Case Study on Surgical Treatment in Operation Room 773.3 Case Study on Waste Management System 803.4 Working Process of the System 813.5 Background Knowledge to be Considered for Estimation 823.5.1 Heuristic Function 833.5.2 Functional Approach 853.6 Case Study on Traveling System 853.7 Future Trends and Conclusion 87References 884 A Hybrid Bat-Genetic Algorithm–Based Novel Optimal Wavelet Filter for Compression of Image Data 89Renjith V. Ravi and Kamalraj Subramaniam4.1 Introduction 904.2 Review of Related Works 914.3 Existing Technique for Secure Image Transmission 934.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression 934.4.1 Optimized Transformation Module 944.4.1.1 DWT Analysis and Synthesis Filter Bank 944.4.2 Compression and Encryption Module 1004.4.2.1 SPIHT 1004.4.2.2 Chaos-Based Encryption 1024.5 Results and Discussion 1044.5.1 Experimental Setup and Evaluation Metrics 1044.5.2 Simulation Results 1074.5.2.1 Performance Analysis of the Novel Filter KARELET 1074.5.3 Result Analysis Proposed System 1084.6 Conclusion 134References 1355 A Swarm Robot for Harvesting a Paddy Field 137N. Pooranam and T. Vignesh5.1 Introduction 1375.1.1 Working Principle of Particle Swarm Optimization 1385.1.2 First Case Study on Birds Fly 1385.1.3 Operational Moves on Birds Dataset 1385.1.4 Working Process of the Proposed Model 1415.2 Second Case Study on Recommendation Systems 1425.3 Third Case Study on Weight Lifting Robot 1455.4 Background Knowledge of Harvesting Process 1495.4.1 Data Flow of PSO Process 1505.4.2 Working Flow of Harvesting Process 1515.4.3 The First Phase of Harvesting Process 1515.4.4 Separation Process in Harvesting 1525.4.5 Cleaning Process in the Field 1525.5 Future Trend and Conclusion 155References 1556 Firefly Algorithm 157Anupriya Jain, Seema Sharma and Sachin Sharma6.1 Introduction 1586.2 Firefly Algorithm 1606.2.1 Firefly Behavior 1606.2.2 Standard Firefly Algorithm 1616.2.3 Variations in Light Intensity and Attractiveness 1636.2.4 Distance and Movement 1646.2.5 Implementation of FA 1656.2.6 Special Cases of Firefly Algorithm 1666.2.7 Variants of FA 1686.3 Applications of Firefly Algorithm 1706.3.1 Job Shop Scheduling 1706.3.2 Image Segmentation 1716.3.3 Stroke Patient Rehabilitation 1726.3.4 Economic Emission Load Dispatch 1726.3.5 Structural Design 1736.4 Why Firefly Algorithm is Efficient 1746.4.1 FA is Not PSO 1766.5 Discussion and Conclusion 176References 1777 The Comprehensive Review for Biobased FPA Algorithm 181Meenakshi Rana7.1 Introduction 1827.1.1 Stochastic Optimization 1837.1.2 Robust Optimization 1837.1.3 Dynamic Optimization 1847.1.4 Alogrithm 1847.1.5 Swarm Intelligence 1857.2 Related Work to FPA 1857.2.1 Flower Pollination Algorithm 1877.2.2 Versions of FPA 1907.2.3 Methods and Description 1907.2.3.1 Reproduction Factor 1937.2.3.2 Levy Flights 1937.2.3.3 User-Defined Parameters 1957.2.3.4 Psuedo Code for FPA 1957.2.3.5 Comparative Studies for FPA 1967.2.3.6 Working Environment 1977.2.3.7 Improved Versions of FPA 1977.3 Limitations 2027.4 Future Research 2027.5 Conclusion 204References 2048 Nature-Inspired Computation in Data Mining 209Aditi Sharma8.1 Introduction 2098.2 Classification of NIC 2118.2.1 Swarm Intelligence for Data Mining 2118.2.1.1 Swarm Intelligence Algorithm 2128.2.1.2 Applications of Swarm Intelligence in Data Mining 2148.2.1.3 Swarm-Based Intelligence Techniques 2148.3 Evolutionary Computation 2278.3.1 Genetic Algorithms 2278.3.1.1 Applications of Genetic Algorithms in Data Mining 2288.3.2 Evolutionary Programming 2288.3.2.1 Applications of Evolutionary Programming in Data Mining 2298.3.3 Genetic Programming 2298.3.3.1 Applications of Genetic Programming in Data Mining 2298.3.4 Evolution Strategies 2308.3.4.1 Applications of Evolution Strategies in Data Mining 2318.3.5 Differential Evolutions 2318.3.5.1 Applications of Differential Evolution in Data Mining 2318.4 Biological Neural Network 2328.4.1 Artificial Neural Computation 2328.4.1.1 Neural Network Models 2328.4.1.2 Challenges of Artificial Neural Network in Data Mining 2338.4.1.3 Applications of Artificial Neural Network in Data Mining 2338.5 Molecular Biology 2338.5.1 Membrane Computing 2338.5.2 Algorithm Basis 2348.5.3 Challenges of Membrane Computing in Data Mining 2348.5.4 Applications of Membrane Computing in Data Mining 2348.6 Immune System 2358.6.1 Artificial Immune System 2358.6.1.1 Artificial Immune System Algorithm (Enhanced) 2368.6.1.2 Challenges of Artificial Immune System in Data Mining 2368.6.1.3 Applications of Artificial Immune System in Data Mining 2378.7 Applications of NIC in Data Mining 2378.8 Conclusion 238References 2389 Optimization Techniques for Removing Noise in Digital Medical Images 243D. Devasena, M. Jagadeeswari, B. Sharmila and K. Srinivasan9.1 Introduction 2449.2 Medical Imaging Techniques 2459.2.1 X-Ray Images 2459.2.2 Computer Tomography Imaging 2459.2.3 Magnetic Resonance Images 2469.2.4 Positron Emission Tomography 2469.2.5 Ultrasound Imaging Techniques 2469.3 Image Denoising 2479.3.1 Impulse Noise and Speckle Noise Denoising 2479.4 Optimization in Image Denoising 2499.4.1 Particle Swarm Optimization 2509.4.2 Adaptive Center Pixel Weighted Median Exponential Filter 2509.4.3 Hybrid Wiener Filter 2519.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization 2529.4.4.1 Curvelet Transform 2529.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter 2539.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter 2559.4.5.1 Dragon Fly Optimization Algorithm 2559.4.5.2 DFOA-Based HWACWMF 2569.5 Results and Discussions 2579.5.1 Simulation Results 2579.5.2 Performance Metric Analysis 2579.5.3 Summary 2639.6 Conclusion and Future Scope 264References 26510 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis 267K. Hariprasath, S. Tamilselvi, N. M. Saravana Kumar, N. Kaviyavarshini and S. Balamurugan10.1 Introduction 26810.1.1 NIC Algorithms 26810.2 Related Works 27010.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) 27410.4 Ten-Fold Cross-Validation 27510.4.1 Training Data 27510.4.2 Validation Data 27510.4.3 Test Data 27610.4.4 Pseudocode 27610.4.5 Advantages of K-Fold or 10-Fold Cross-Validation 27610.5 Naive Bayesian Classifier 27610.5.1 Pseudocode of Naive Bayesian Classifier 27810.5.2 Advantages of Naive Bayesian Classifier 27810.6 K-Means Clustering 27910.7 Support Vector Machine (SVM) 28010.8 Swarm Intelligence Algorithms 28210.8.1 Particle Swarm Optimization 28310.8.2 Firefly Algorithm 28510.8.3 Ant Colony Optimization 28710.9 Evaluation Metrics 28810.10 Results and Discussion 28910.11 Conclusion 291References 29211 Applications of Cuckoo Search Algorithm for Optimization Problems 295Akanksha Deep and Prasant Kumar Dash11.1 Introduction 29611.2 Related Works 29811.3 Cuckoo Search Algorithm 29911.3.1 Biological Description 30011.3.2 Algorithm 30011.4 Applications of Cuckoo Search 30411.4.1 In Engineering 30511.4.1.1 Applications in Mechanical Engineering 30511.4.2 In Structural Optimization 30811.4.2.1 Test Problems 30811.4.3 Application CSA in Electrical Engineering, Power, and Energy 30811.4.3.1 Embedded System 30811.4.3.2 PCB 30911.4.3.3 Power and Energy 30911.4.4 Applications of CS in Field of Machine Learning and Computation 31011.4.5 Applications of CS in Image Processing 31111.4.6 Application of CSA in Data Processing 31111.4.7 Applications of CSA in Computation and Neural Network 31211.4.8 Application in Wireless Sensor Network 31311.5 Conclusion and Future Work 314References 31512 Mapping of Real-World Problems to Nature-Inspired Algorithm Using Goal-Based Classification and TRIZ 317Palak Sukharamwala and Manojkumar Parmar12.1 Introduction and Background 31812.2 Motivations Behind NIA Exploration 31912.2.1 Prevailing Issues With Technology 31912.2.1.1 Data Dependencies 31912.2.1.2 Demand for Higher Software Complexity 32012.2.1.3 NP-Hard Problems 32012.2.1.4 Energy Consumption 32112.2.2 Nature-Inspired Algorithm at a Rescue 32112.3 Novel TRIZ + NIA Approach 32212.3.1 Traditional Classification 32212.3.1.1 Swarm Intelligence 32212.3.1.2 Evolution Algorithm 32312.3.1.3 Bio-Inspired Algorithms 32412.3.1.4 Physics-Based Algorithm 32412.3.1.5 Other Nature-Inspired Algorithms 32412.3.2 Limitation of Traditional Classification 32412.3.3 Combined Approach NIA + TRIZ 32512.3.3.1 TRIZ 32512.3.3.2 NIA + TRIZ 32512.3.4 End Goal–Based Classification 32612.4 Examples to Support the TRIZ + NIA Approach 32712.4.1 Fruit Optimization Algorithm to Predict Monthly Electricity Consumption 32712.4.2 Bat Algorithm to Model River Dissolved Oxygen Concentration 33212.4.3 Genetic Algorithm to Tune the Structure and Parameters of a Neural Network 33312.5 A Solution of NP-H Using NIA 33512.5.1 The 0-1 Knapsack Problem 33512.5.2 Traveling Salesman Problem 33712.6 Conclusion 338References 338Index 341