| Description |
1 online resource (383 p.) |
| Series |
Computing and Networks Series |
|
Computing and Networks Series
|
| Contents |
Intro -- Title -- Copyright -- 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 Physics- and 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 |
| Summary |
This edited book reviews the intertwining disciplines of nature-inspired optimization algorithms and bio-inspired soft-computing for real world applications, with the interaction between metaheuristics with complex systems. The authors present methods and techniques in IoT, image processing, smart manufacturing and healthcare |
| Notes |
Description based upon print version of record |
|
3.6.5 Feature selection based on CS |
| Form |
Electronic book
|
| Author |
Singh, Sangeeta
|
|
Singh, Maheshwari P
|
|
Iyer, Brijesh R
|
|
Gudivada, Venkat N
|
| ISBN |
9781839535178 |
|
1839535172 |
|
