We have witnessed an explosion of research activity around nature-inspired computing and bio-inspired optimization techniques, which can provide powerful tools for solving learning problems and data analysis in very large data sets. To design and implement optimization algorithms, several methods are used that bring superior performance. However, in some applications, the search space increases exponentially with the problem size. To overcome these limitations and to solve efficiently large scale combinatorial and highly nonlinear optimization problems, more flexible and adaptable algorithms are necessary.
Nature-inspired computing is oriented towards the application of outstanding information-processing aptitudes of the natural realm to the computational domain. The discipline of nature-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization. Metaheuristic search algorithms with population-based frameworks are capable of handling optimization in high-dimensional real-world problems for several domains including imaging, IoT, smart manufacturing, and healthcare. The integration of intelligence with smart technology enhances accuracy and efficiency. Smart devices and systems are revolutionizing the world by linking innovative thinking with innovative action and innovative implementation.
The aim of this edited book is to review the intertwining disciplines of nature-inspired computing and bio-inspired soft-computing (BISC) and their applications to real world challenges. The contributors cover the interaction between metaheuristics, such as evolutionary algorithms and swarm intelligence, with complex systems. They explain how to better handle different kinds of uncertainties in real-life problems using state-of-art of machine learning algorithms. They also explore future research perspectives to bridge the gap between theory and real-life day-to-day challenges for diverse domains of engineering.
Author(s): Rajeev Arya, Sangeeta Singh, Maheshwari P. Singh, Brijesh R. Iyer and Venkat N. Gudivada
Publisher: The Institution of Engineering and Technology
Year: 2023
Language: English
Pages: 298
Cover
Contents
About the editors
Foreword
Preface
1 Introduction to various optimization techniques
1.1 Introduction
1.2 Optimization
1.3 Search for optimality
1.4 Needs for optimization
1.5 A brief history of metaheuristics optimization
1.6 Difference between metaheuristics optimization and heuristic optimization
1.7 Implications of metaheuristic optimization
1.8 Heuristic optimization algorithms
1.8.1 Constructive heuristic optimization algorithms
1.9 Metaheuristics optimization algorithms
1.9.1 Trajectory-based metaheuristic algorithms
1.9.2 Population-based metaheuristic algorithms
1.10 Theoretical analysis
1.11 Systematic approach for the selection of optimization algorithms
References
2 Nature-inspired optimization algorithm: an in-depth view
2.1 Introduction to nature-inspired algorithm
2.2 Search for an ideal algorithm
2.3 Extensive review of nature-inspired algorithm
2.4 Analysis of nature-inspired algorithms
2.5 Classes of optimization algorithm
2.6 General classification of nature-inspired algorithms
2.7 Evolutionary algorithms
2.7.1 Genetic algorithm
2.7.2 Differential evolution
2.8 Bio-inspired algorithms
2.8.1 Swarm intelligence-based bio-inspired algorithms
2.8.2 Variants of swarm algorithms
2.8.3 Bio-inspired but not swarm intelligence based
2.9 Physicsand chemistry-based algorithm
2.9.1 WCA
2.10 Application of nature-inspired optimization algorithm on constraints engineering problem
2.10.1 Nature-inspired optimization algorithm (NIOA)based clustering routing protocols
2.10.2 Implementation of WCA on leach routing protocol
2.10.3 Nature-inspired optimization algorithm applied in solid-state wielding
2.11 Conclusion
References
3 Application aspects of nature-inspired optimization algorithms
3.1 Introduction
3.2 Application domains of nature-inspired optimization algorithms
3.2.1 Optimization in image denoising
3.2.2 Optimization in image enhancement
3.2.3 Optimization in image segmentation
3.2.4 Optimization in image feature extraction and selection
3.2.5 Optimization in image classification
3.3 Implementation
3.3.1 Parameter tuning
3.3.2 Manual tuning
3.3.3 Grid search
3.3.4 Random search
3.3.5 Metaheuristic optimization
3.4 Constrained and unconstrained optimization
3.4.1 Constrained optimization
3.4.2 Unconstrained optimization
3.5 How to deal with constraints
3.5.1 Penalty functions
3.5.2 Linear penalty function
3.5.3 Quadratic penalty function
3.5.4 Constraint handling techniques
3.5.5 Hybrid constraint handling techniques
3.6 Feature selection
3.6.1 Feature selection based on GA
3.6.2 Feature selection based on PSO
3.6.3 Feature selection based on ACO
3.6.4 Feature selection based on ABC
3.6.5 Feature selection based on CS
3.6.6 Feature selection based on FF
3.7 Practical engineering applications
3.8 Conclusion
List of Abbreviations
References
4 Particle swarm optimization applications and implications
4.1 Introduction to PSO
4.1.1 PSO elements
4.1.2 PSO algorithm
4.1.3 Standard pseudo code
4.1.4 PSO advantages and disadvantages
4.1.5 PSO applications
4.2 Outline of swarm intelligence
4.2.1 General swarm principles
4.3 PSO for single-objective problem
4.4 PSO for multi-objective problem
4.5 Different approaches of multi-objective PSO
4.5.1 Objective function aggregation approach
4.5.2 Objective function ordering approach
4.5.3 Non-Pareto, vector-evaluated approach
4.5.4 Algorithms based on Pareto dominance
4.6 Variants of PSO algorithm
4.6.1 Discrete PSO
4.6.2 Binary PSO
4.6.3 Adaptive PSO
4.6.4 Hybrid PSO
4.6.5 Neighborhood-guaranteed convergence PSO
4.6.6 Neighborhood search strategies PSO
4.6.7 Immunity-enhanced PSO
4.7 PSO in hybrid environment
4.8 Computational experiments
4.9 Convergence
4.10 PSO implications on image processing problems
4.11 PSO implications on optimum route-finding problems
4.12 Implementation and results
References
5 Advanced optimization by nature-inspired algorithm
5.1 Introduction
5.2 List of nature-inspired algorithms
5.3 Optimization techniques
5.3.1 Anarchic society optimization (ASO)
5.3.2 Antlion optimizer (ALO)
5.3.3 Cat swarm optimization
5.3.4 Crow search algorithm
5.3.5 Cuckoo search
5.3.6 Mine blast algorithm
5.3.7 Water cycle algorithm
5.4 Conclusion
References
6 Application and challenges of optimization in Internet of Things (IoT)
6.1 Introduction
6.2 Application of optimization in the IoT
6.2.1 Challenges of optimization in IoT
6.3 Network optimization in IoT
6.3.1 Types of network optimization in IOT
6.3.2 Algorithms of network optimization in IoT
6.3.3 Advantages of network optimization in IoT
6.3.4 Disadvantages of network optimization in IoT
6.4 Nature-inspired optimization in IoT
6.4.1 Algorithms of nature-inspired optimization in IoT
6.4.2 Role of nature-inspired algorithms in IoT
6.4.3 Advantages of nature-inspired optimization in IoT
6.4.4 Disadvantages of nature-inspired optimization in IoT
6.5 Evolutionary algorithms in IoT
6.5.1 Algorithms of evolutionary optimization in the IoT
6.5.2 Role of evolutionary algorithms in IoT
6.5.3 Advantages of evolutionary optimization in IoT
6.5.4 Disadvantages of evolutionary optimization in IoT
6.6 Bio-inspired heuristic algorithms in IoT
6.6.1 Types of bio-inspired heuristic algorithm in IoT
6.6.2 Role of bio-inspired heuristic algorithm in IoT
6.6.3 Advantages of bio-inspired heuristic algorithm in IoT
6.6.4 Limitations of bio-inspired heuristic algorithm in IoT
6.7 Load optimization in cognitive IoT
6.7.1 Uses of load optimization in cognitive IoT
6.7.2 Advantages of load optimization in cognitive IoT
6.7.3 Disadvantages of load optimization in cognitive IoT
6.8 Comparative analysis
References
7 Optimization applications and implications in biomedicines and healthcare
7.1 Introduction
7.2 Role of optimization algorithms in healthcare systems
7.3 Optimization algorithms in medical diagnosis
7.4 Optimization algorithms in biomedical informatics
7.5 Optimization algorithms in biomedical image processing
7.6 Optimization algorithms for ECG classification
7.7 Feature extraction and classification
7.7.1 Case study 1: feature extraction in mammography
7.7.2 Features are extracted by discrete wavelet transform
7.7.3 Features are extracted by the Gabor filter
7.7.4 Case study II: feature extraction in speech and pattern recognition
7.7.5 Feature selection
7.7.6 Classification
7.8 Optimization algorithm-based intelligent detection of heart disorders
7.9 Using predictive analytics in healthcare
7.10 Optimization algorithm for smart healthcare: innovations and technologies
7.11 Issues and challenges in using optimization algorithms for smart healthcare and wearables
List of abbreviations
References
8 Applications and challenges of optimization in industrial automation
8.1 Factory digitalization
8.1.1 Birth of factory automation
8.2 Product flow monitoring
8.2.1 Creating applications with monitoring in mind from the start
8.2.2 Organize products into several categories
8.2.3 Include real-time tracking technologies
8.3 Inventory management
8.3.1 Time efficiency
8.3.2 Scalability
8.3.3 Accuracy
8.3.4 Synchronization
8.3.5 Quality of delivery (QoD)
8.4 Safety and security
8.5 Quality control
8.6 Packaging optimization
8.7 Logistics and supply chain optimization
References
9 Expectations from modern evolutionary approaches for image processing
9.1 Application domains of nature-inspired optimization algorithms
9.1.1 Implementation
9.1.2 Finding optimized threshold level using harmony search optimization algorithm
9.2 Results
9.3 Parameter tanning
9.4 Constrained and unconstrained optimization
9.5 How to deal with constraints
9.6 Feature selection
9.7 Advantages of using optimization techniques in engineering applications
9.8 Conclusion
References
10 Conclusion
10.1 Concluding remarks
10.2 Challenges and potentials of bio-inspired optimization algorithms for IoT applications
10.2.1 Challenges
10.2.2 Potentials
10.3 Challenges and opportunities of bio-inspired optimization algorithms for biomedical applications
10.4 Recent trends in smart cities planning based on nature-inspired computing
10.5 Future perspectives of nature-inspired computing
10.6 Bio-inspired heuristic algorithms
10.7 Probable future directions
References
Index
Back Cover
