Nature Inspired Algorithms and Their Applications

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
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
	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 Algorithm:
		Pseudo Algorithm:
		Implementation:
		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
		3.1.4 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
		4.4.2 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
	7.3 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
	8.3 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
		11.4.2 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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