Оглавление
Группа авторов. Nature-Inspired Algorithms and Applications
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Nature-Inspired Algorithms Applications
Preface
1. Introduction to Nature-Inspired Computing
1.1 Introduction
1.2 Aspiration From Nature
1.3 Working of Nature
1.4 Nature-Inspired Computing
1.4.1 Autonomous Entity
1.5 General Stochastic Process of Nature-Inspired Computation
1.5.1 NIC Categorization
1.5.1.1 Bioinspired Algorithm
1.5.1.2 Swarm Intelligence
1.5.1.3 Physical Algorithms
1.5.1.4 Familiar NIC Algorithms. 1.5.1.4.1 Boids
1.5.1.4.2 Memetic Algorithm
1.5.1.4.3 Evolutionary Algorithms
1.5.1.4.4 Genetic Algorithm
1.5.1.4.5 Ant Colony Optimization
1.5.1.4.6 Particle Swarm Optimization
1.5.1.4.7 Harmony Search
1.5.1.4.8 Social Cognitive Optimization
1.5.1.4.9 Artificial Bee Colony Algorithm
1.5.1.4.10 River Formation Dynamics
1.5.1.4.11 Firefly Algorithm
1.5.1.4.12 Group Search Optimizer Algorithm
1.5.1.4.13 Bat Algorithm
1.5.1.4.14 Binary Bat Algorithm
1.5.1.4.15 Cuttlefish Algorithm
1.5.1.4.16 Grey Wolf Optimizer
1.5.1.4.17 Elephant Herding Optimization
References
2. Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning
2.1 Introduction of Genetic Algorithm
2.1.1 Background of GA
2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm?
2.1.3 Working Sequence of Genetic Algorithm
2.1.3.1 Population
2.1.3.2 Fitness Among the Individuals
2.1.3.3 Selection of Fitted Individuals
2.1.3.4 Crossover Point
2.1.3.5 Mutation
2.1.4 Application of Machine Learning in GA
2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem
2.1.4.2 Traveling Salesman Problem
2.1.4.3 Blackjack—A Casino Game
2.1.4.4 Pong Against AI—Evolving Agents (Reinforcement Learning) Using GA
2.1.4.5 SNAKE AI—Game
2.1.4.6 Genetic Algorithm’s Role in Neural Network
2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967
2.1.4.8 Frozen Lake Problem From OpenAI Gym
2.1.4.9 N-Queen Problem
2.1.5 Application of Data Mining in GA
2.1.5.1 Association Rules Generation
2.1.5.2 Pattern Classification With Genetic Algorithm
2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization
2.1.5.4 Market Basket Analysis
2.1.5.5 Job Scheduling
2.1.5.6 Classification Problem
2.1.5.7 Hybrid Decision Tree—Genetic Algorithm to Data Mining
2.1.5.8 Genetic Algorithm—Optimization of Data Mining in Education
2.1.6 Advantages of Genetic Algorithms
2.1.7 Genetic Algorithms Demerits in the Current Era
2.2 Introduction to Artificial Bear Optimization (ABO)
2.2.1 Bear’s Nasal Cavity
2.2.2 Artificial Bear ABO Gist
2.2.3 Implementation Based on Requirement
2.2.3.1 Market Place
2.2.3.2 Industry-Specific
2.2.3.3 Semi-Structured or Unstructured Data
2.2.4 Merits of ABO
2.3 Performance Evaluation
2.4 What is Next?
References
3. Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique
3.1 Introduction
3.1.1 Example of Optimization Process
3.1.2 Components of Optimization Algorithms
3.1.3 Optimization Techniques Based on Solutions
3.1.3.1 Optimization Techniques Based on Algorithms
3.1.3.1.1 Exact Algorithms
3.1.4 Characteristics
3.1.5 Classes of Heuristic Algorithms
3.1.6 Metaheuristic Algorithms
3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature–Inspired
3.1.6.2 Population-Based vs. Single-Point Search (Trajectory)
3.1.7 Data Processing Flow of ACO
3.2 A Case Study on Surgical Treatment in Operation Room
3.3 Case Study on Waste Management System
3.4 Working Process of the System
3.5 Background Knowledge to be Considered for Estimation
3.5.1 Heuristic Function
3.5.2 Functional Approach
3.6 Case Study on Traveling System
3.7 Future Trends and Conclusion
References
4. A Hybrid Bat-Genetic Algorithm–Based Novel Optimal Wavelet Filter for Compression of Image Data
4.1 Introduction
4.2 Review of Related Works
4.3 Existing Technique for Secure Image Transmission
4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression
4.4.1 Optimized Transformation Module
4.4.1.1 DWT Analysis and Synthesis Filter Bank
4.4.1.1.1 Bat-Genetic Hybridization
4.4.2 Compression and Encryption Module
4.4.2.1 SPIHT
4.4.2.2 Chaos-Based Encryption
4.5 Results and Discussion
4.5.1 Experimental Setup and Evaluation Metrics
4.5.2 Simulation Results. 4.5.2.1 Performance Analysis of the Novel Filter KARELET
4.5.3 Result Analysis Proposed System
4.6 Conclusion
References
5. A Swarm Robot for Harvesting a Paddy Field
5.1 Introduction
5.1.1 Working Principle of Particle Swarm Optimization
5.1.2 First Case Study on Birds Fly
5.1.3 Operational Moves on Birds Dataset
5.1.4 Working Process of the Proposed Model
5.2 Second Case Study on Recommendation Systems
5.3 Third Case Study on Weight Lifting Robot
5.4 Background Knowledge of Harvesting Process
5.4.1 Data Flow of PSO Process
5.4.2 Working Flow of Harvesting Process
5.4.3 The First Phase of Harvesting Process
5.4.4 Separation Process in Harvesting
5.4.5 Cleaning Process in the Field
5.5 Future Trend and Conclusion
References
6. Firefly Algorithm
6.1 Introduction
6.2 Firefly Algorithm
6.2.1 Firefly Behavior
6.2.2 Standard Firefly Algorithm
6.2.3 Variations in Light Intensity and Attractiveness
6.2.4 Distance and Movement
6.2.5 Implementation of FA
6.2.6 Special Cases of Firefly Algorithm
6.2.7 Variants of FA
6.3 Applications of Firefly Algorithm
6.3.1 Job Shop Scheduling
6.3.2 Image Segmentation
6.3.3 Stroke Patient Rehabilitation
6.3.4 Economic Emission Load Dispatch
6.3.5 Structural Design
6.4 Why Firefly Algorithm is Efficient
6.4.1 FA is Not PSO
6.5 Discussion and Conclusion
References
7. The Comprehensive Review for Biobased FPA Algorithm
7.1 Introduction
7.1.1 Stochastic Optimization
7.1.2 Robust Optimization
7.1.3 Dynamic Optimization
7.1.4 Alogrithm
7.1.5 Swarm Intelligence
7.2 Related Work to FPA
7.2.1 Flower Pollination Algorithm
7.2.2 Versions of FPA
7.2.3 Methods and Description
7.2.3.1 Reproduction Factor
7.2.3.2 Levy Flights
7.2.3.3 User-Defined Parameters
7.2.3.4 Psuedo Code for FPA
7.2.3.5 Comparative Studies for FPA
7.2.3.6 Working Environment
7.2.3.7 Improved Versions of FPA
7.2.3.7.1 Modified FPA (MFPA)
7.2.3.7.2 Improved FPA With Chaos (IFPCH)
7.2.3.7.3 Application of FPA
7.3 Limitations
7.4 Future Research
7.5 Conclusion
References
8. Nature-Inspired Computation in Data Mining
8.1 Introduction
8.2 Classification of NIC
8.2.1 Swarm Intelligence for Data Mining
8.2.1.1 Swarm Intelligence Algorithm
8.2.1.2 Applications of Swarm Intelligence in Data Mining
8.2.1.3 Swarm-Based Intelligence Techniques
8.2.1.3.1 Ant Colony Optimization
8.2.1.3.1.1 ANT COLONY OPTIMIZATION ALGORITHM
8.2.1.3.1.2 APPLICATION OF ACO IN DATA MINING
8.2.1.3.1.3 CHALLENGES IN ACO-BASED DATA MINING
8.2.1.3.2 Particle Swarm Optimization
8.2.1.3.2.1 PARTICLE SWARM OPTIMIZATION ALGORITHM
8.2.1.3.2.2 APPLICATIONS OF PSO IN DATA MINING
8.2.1.3.2.3 CHALLENGES OF PSO IN DATA MINING
8.2.1.3.3 Cuckoo Search
8.2.1.3.3.1 CUCKOO SEARCH ALGORITHM
8.2.1.3.3.2 CHALLENGES OF CUCKOO SEARCH IN DATA MINING
8.2.1.3.3.3 APPLICATIONS OF CUCKOO SEARCH IN DATA MINING
8.2.1.3.4 Biogeography-Based Optimization
8.2.1.3.4.1 BIOGEOGRAPHY-BASED OPTIMIZATION ALGORITHM (PSEUDO)
8.2.1.3.4.2 CHALLENGES OF BBO IN DATA MINING
8.2.1.3.4.3 APPLICATIONS OF BBO IN DATA MINING
8.2.1.3.5 Cat Swarm Optimization
8.2.1.3.5.1 CAT SWARM OPTIMIZATION ALGORITHM
8.2.1.3.5.2 CHALLENGES OF CSO IN DATA MINING
8.2.1.3.5.3 APPLICATIONS OF CSO IN DATA MINING
8.2.1.3.5.4 APPLICATIONS OF CAT SWARM OPTIMIZATION IN DATA MINING
8.3 Evolutionary Computation
8.3.1 Genetic Algorithms
8.3.1.1 Applications of Genetic Algorithms in Data Mining
8.3.2 Evolutionary Programming
8.3.2.1 Applications of Evolutionary Programming in Data Mining
8.3.3 Genetic Programming
8.3.3.1 Applications of Genetic Programming in Data Mining
8.3.4 Evolution Strategies
8.3.4.1 Applications of Evolution Strategies in Data Mining
8.3.5 Differential Evolutions
8.3.5.1 Applications of Differential Evolution in Data Mining
8.4 Biological Neural Network
8.4.1 Artificial Neural Computation
8.4.1.1 Neural Network Models
8.4.1.2 Challenges of Artificial Neural Network in Data Mining
8.4.1.3 Applications of Artificial Neural Network in Data Mining
8.5 Molecular Biology
8.5.1 Membrane Computing
8.5.2 Algorithm Basis
8.5.3 Challenges of Membrane Computing in Data Mining
8.5.4 Applications of Membrane Computing in Data Mining
8.6 Immune System
8.6.1 Artificial Immune System
8.6.1.1 Artificial Immune System Algorithm (Enhanced)
8.6.1.2 Challenges of Artificial Immune System in Data Mining
8.6.1.3 Applications of Artificial Immune System in Data Mining
8.7 Applications of NIC in Data Mining
8.8 Conclusion
References
9. Optimization Techniques for Removing Noise in Digital Medical Images
9.1 Introduction
9.2 Medical Imaging Techniques
9.2.1 X-Ray Images
9.2.2 Computer Tomography Imaging
9.2.3 Magnetic Resonance Images
9.2.4 Positron Emission Tomography
9.2.5 Ultrasound Imaging Techniques
9.3 Image Denoising
9.3.1 Impulse Noise and Speckle Noise Denoising
9.4 Optimization in Image Denoising
9.4.1 Particle Swarm Optimization
9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter
9.4.3 Hybrid Wiener Filter
9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization
9.4.4.1 Curvelet Transform
9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter
9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter
9.4.5.1 Dragon Fly Optimization Algorithm
9.4.5.2 DFOA-Based HWACWMF
9.5 Results and Discussions
9.5.1 Simulation Results
9.5.2 Performance Metric Analysis
9.5.3 Summary
9.6 Conclusion and Future Scope
References
10. Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis
10.1 Introduction
10.1.1 NIC Algorithms
10.2 Related Works
10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD)
10.4 Ten-Fold Cross-Validation
10.4.1 Training Data
10.4.2 Validation Data
10.4.3 Test Data
10.4.4 Pseudocode
10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation
10.5 Naive Bayesian Classifier
10.5.1 Pseudocode of Naive Bayesian Classifier
10.5.2 Advantages of Naive Bayesian Classifier
10.6 K-Means Clustering
10.7 Support Vector Machine (SVM)
10.8 Swarm Intelligence Algorithms
10.8.1 Particle Swarm Optimization
10.8.2 Firefly Algorithm
10.8.3 Ant Colony Optimization
10.9 Evaluation Metrics
10.10 Results and Discussion
10.11 Conclusion
References
11. Applications of Cuckoo Search Algorithm for Optimization Problems
11.1 Introduction
11.2 Related Works
11.3 Cuckoo Search Algorithm
11.3.1 Biological Description
11.3.2 Algorithm
11.4 Applications of Cuckoo Search
11.4.1 In Engineering
11.4.1.1 Applications in Mechanical Engineering. 11.4.1.1.1 Laser Cutting
11.4.1.1.2 Four-Bar Mechanism
11.4.1.1.3 Trajectory Planning of Quadrotar
11.4.2 In Structural Optimization
11.4.2.1 Test Problems
11.4.3 Application CSA in Electrical Engineering, Power, and Energy. 11.4.3.1 Embedded System
11.4.3.2 PCB
11.4.3.3 Power and Energy
11.4.4 Applications of CS in Field of Machine Learning and Computation
11.4.5 Applications of CS in Image Processing
11.4.6 Application of CSA in Data Processing
11.4.7 Applications of CSA in Computation and Neural Network
11.4.8 Application in Wireless Sensor Network
11.5 Conclusion and Future Work
References
12. Mapping of Real-World Problems to Nature-Inspired Algorithm Using Goal-Based Classification and TRIZ
12.1 Introduction and Background
12.2 Motivations Behind NIA Exploration
12.2.1 Prevailing Issues With Technology. 12.2.1.1 Data Dependencies
12.2.1.2 Demand for Higher Software Complexity
12.2.1.3 NP-Hard Problems
12.2.1.4 Energy Consumption
12.2.2 Nature-Inspired Algorithm at a Rescue
12.3 Novel TRIZ + NIA Approach. 12.3.1 Traditional Classification
12.3.1.1 Swarm Intelligence
12.3.1.2 Evolutionary Algorithm
12.3.1.3 Bio-Inspired Algorithms
12.3.1.4 Physics-Based Algorithm
12.3.1.5 Other Nature-Inspired Algorithms
12.3.2 Limitation of Traditional Classification
12.3.3 Combined Approach NIA + TRIZ
12.3.3.1 TRIZ
12.3.3.2 NIA + TRIZ
12.3.4 End Goal–Based Classification
12.4 Examples to Support the TRIZ + NIA Approach. 12.4.1 Fruit Optimization Algorithm to Predict Monthly Electricity Consumption
12.4.2 Bat Algorithm to Model River Dissolved Oxygen Concentration
12.4.3 Genetic Algorithm to Tune the Structure and Parameters of a Neural Network
12.5 A Solution of NP-H Using NIA. 12.5.1 The 0-1 Knapsack Problem
12.5.2 Traveling Salesman Problem
12.6 Conclusion
References
Index
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