Nature Inspired Computing and Optimization Theory and Applications 1st Edition by Srikanta Patnaik 3319509204 9783319509204

Nature Inspired Computing and Optimization Theory and Applications 1st Edition by Srikanta Patnaik – Ebook PDF Instant Download/DeliveryISBN:  3319509204, 9783319509204 

Full download Nature Inspired Computing and Optimization Theory and Applications 1st Edition after payment.

Product details:

ISBN-10 :  3319509204 

ISBN-13 : 9783319509204

Author: Srikanta Patnaik 

The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.

Nature Inspired Computing and Optimization Theory and Applications 1st Table of contents:

1 Introduction: How Nature Works
2 The Nature of Nature
2.1 Fitness Landscape
2.2 Graphs and Phase Changes
3 Nature-Inspired Algorithms
3.1 Genetic Algorithm
3.2 Ant Colony Optimization
3.3 Simulated Annealing
3.4 Convergence
4 Dual-Phase Evolution
4.1 Theory
4.2 GA
4.3 Ant Colony Optimization
4.4 Simulated Annealing
5 Evolutionary Dynamics
5.1 Markov Chain Models
5.2 The Replicator Equation
6 Generalized Local Search Machines
6.1 The Model
6.2 SA
6.3 GA
6.4 ACO
6.5 Discussion
7 Conclusion
References
Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environme
1 Introduction
2 Improved Bat Algorithm
3 IBA for Multimodal Problems
3.1 Parameter Settings
3.2 Test Functions
3.3 Numerical Results
4 Performance Comparison of IBA with Other Algorithms
5 IBA Performance in AWGN
5.1 Numerical Results
6 Conclusions
References
Multi-objective Ant Colony Optimisation in Wireless Sensor Networks
1 Introduction
2 Multi-objective Combinatorial Optimisation Problems
2.1 Combinatorial Optimisation Problems
2.2 Multi-objective Combinatorial Optimisation Problems
2.3 Pareto Optimality
2.4 Decision-Making
2.5 Solving Combinatorial Optimisation Problems
3 Multi-objective Ant Colony Optimisation
3.1 Origins
3.2 Multi-objective Ant Colony Optimisation
4 Applications of MOACO Algorithms in WSNs
5 Conclusion
References
Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence
1 Introduction
2 Artificial Sports Trainer
3 Generating the Training Plans
3.1 Preprocessing
3.2 Optimization Process
4 Experiments
5 Conclusion with Future Ideas
References
Limiting Distribution and Mixing Time for Genetic Algorithms
1 Introduction
2 Preliminaries
2.1 Random Search and Markov Chains
2.2 Boltzmann Distribution and Simulated Annealing
3 Expected Hitting Time as a Means of Comparison
3.1 “No Free Lunch” Considerations
4 The Holland Genetic Algorithm
5 A Simple Genetic Algorithm
6 Shuffle-Bit GA
6.1 Results
6.2 Estimate of Expected Hitting Time
7 Discussion and Future Work
References
Permutation Problems, Genetic Algorithms, and Dynamic Representations
1 Introduction
2 Problem Descriptions
2.1 Bin Packing Problem
2.2 Graph Colouring Problem
2.3 Travelling Salesman Problem
3 Previous Work on Small Travelling Salesman Problem Instances
4 Algorithms
4.1 2-Opt
4.2 Lin–Kernighan
4.3 Genetic Algorithm Variations
4.4 Representation
5 Experimental Design
5.1 Bin Packing Problem
5.2 Graph Colouring Problem
5.3 Travelling Salesman Problem
6 Results and Discussion
6.1 Bin Packing Problem
6.2 Graph Colouring Problem
6.3 Travelling Salesman Problem
7 Conclusions
References
Hybridization of the Flower Pollination Algorithm—A Case Study in the Problem of Generating Healt
1 Introduction
2 Background
2.1 Optimization Problems
2.2 Meta-Heuristic Algorithms
3 Literature Review
4 Problem Definition
4.1 Search Space and Solution Representation
4.2 Fitness Function
4.3 Constraints
5 Hybridizing the Flower Pollination Algorithm for Generating Personalized Menu Recommendations
5.1 Hybrid Flower Pollination-Based Model
5.2 Flower Pollination-Based Algorithms for Generating Personalized Menu Recommendations
5.3 The Iterative Stage of the Hybrid Flower Pollination-Based Algorithm for Generating Healthy Menu
6 Performance Evaluation
6.1 Experimental Prototype
6.2 Test Scenarios
6.3 Setting the Optimal Values of the Algorithms’ Adjustable Parameters
6.4 Comparison Between the Classical and Hybrid Flower Pollination-Based Algorithms
7 Conclusions
References
Nature-inspired Algorithm-based Optimization for Beamforming of Linear Antenna Array System
1 Introduction
2 Problem Formulation
3 Flower Pollination Algorithm [55]3.1 Global Pollination:
3.2 Local Pollination:
3.3 Pseudo-code for FPA:
4 Simulation Results
4.1 Optimization of Hyper-Beam by Using FPA
4.2 Comparisons of Accuracies Based on t test
5 Convergence Characteristics of Different Algorithms
6 Conclusion
7 Future Research Topics
References
Multi-Agent Optimization of Resource-Constrained Project Scheduling Problem Using Nature-Inspired
1 Introduction
1.1 Multi-agent System
1.2 Scheduling
1.3 Nature-Inspired Computing
2 Resource-Constrained Project Scheduling Problem
3 Various Nature-Inspired Computation Techniques for RCPSP
3.1 Particle Swarm Optimization (PSO)
3.2 Particle Swarm Optimization (PSO) for RCPSP
3.3 Ant Colony Optimization (ACO)
3.4 Ant Colony Optimization (ACO) for RCPSP
3.5 Shuffled Frog-Leaping Algorithm (SFLA)
3.6 Shuffled Frog-Leaping Algorithm (SFLA) for RCPSP
3.7 Multi-objective Invasive Weed Optimization
3.8 Multi-objective Invasive Weed Optimization for MRCPSP
3.9 Discrete Flower Pollination
3.10 Discrete Flower Pollination for RCPSP
3.11 Discrete Cuckoo Search
3.12 Discrete Cuckoo Search for RCPSP
3.13 Multi-agent Optimization Algorithm (MAOA)
4 Proposed Approach
4.1 RCPSP for Retail Industry
4.2 Cooperative Hunting Behaviour of Lion Pride
5 A Lion Pride-Inspired Multi-Agent System-Based Approach for RCPSP
6 Conclusion
References
Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology
1 Introduction
2 Learning Classifier Systems: Creating Rules that Describe Systems
2.1 Basic Components
2.2 Michigan- and Pittsburgh-style LCS
3 Examples of LCS
3.1 Minimal Classifier Systems
3.2 Zeroth-level Classifier Systems
3.3 Extended Classifier Systems
4 Synthetic Biology: Designing Biological Systems
4.1 The Synthetic Biology Design Cycle
4.2 Basic Biological Parts
4.3 DNA Construction
4.4 Future Applications
5 Gene Expression Analysis with LCS
6 Optimization of Artificial Operon Structure
7 Optimization of Artificial Operon Construction by Machine Learning
7.1 Introduction
7.2 Artificial Operon Model
7.3 Experimental Framework
7.4 Results
7.5 Conclusion
8 Summary
References
Ant Colony Optimization for Semantic Searching of Distributed Dynamic Multiclass Resources
1 Introduction
2 P2p Search Strategies
3 Nature-Inspired Ant Colony Optimization
4 Nature-Inspired Strategies in Dynamic Networks
4.1 Network Dynamism Inefficiency
4.2 Solution Framework
4.3 Experimental Evaluation
5 Nature-Inspired Strategies of Semantic Nature
5.1 Semantic Query Inefficiency
5.2 Solution Framework
5.3 Experimental Evaluation
6 Conclusions and Future Developments
References
Adaptive Virtual Topology Control Based on Attractor Selection
1 Introduction
2 Related Work
3 Attractor Selection
3.1 Concept of Attractor Selection
3.2 Cell Model
3.3 Mathematical Model of Attractor Selection
4 Virtual Topology Control Based on Attractor Selection
4.1 Virtual Topology Control
4.2 Overview of Virtual Topology Control Based on Attractor Selection
4.3 Dynamics of Virtual Topology Control
4.4 Attractor Structure
4.5 Dynamic Reconfiguration of Attractor Structure
5 Performance Evaluation
5.1 Simulation Conditions
5.2 Dynamics of Virtual Topology Control Based on Attractor Selection
5.3 Adaptability to Node Failures
5.4 Effects of Noise Strength
5.5 Effects of Activity
5.6 Effects of Reconfiguration Methods of Attractor Structure
6 Conclusion
References
CBO-Based TDR Approach for Wiring Network Diagnosis
1 Introduction
2 The Proposed TDR-CBO-Based Approach
2.1 Problem Formulation
2.2 The Forward Model
2.3 Colliding Bodies Optimization (CBO)
3 Applications and Results
3.1 The Y-Shaped Wiring Network
3.2 The YY-shaped Wiring Network
4 Conclusion
References
Morphological Filters: An Inspiration from Natural Geometrical Erosion and Dilation
1 Natural Geometrical Inspired Operators
2 Mathematical Morphology
2.1 Morphological Filters
3 Morphological Operators and Set Theory
3.1 Sets and Corresponding Operators
3.2 Basic Properties for Morphological Operators
3.3 Set Dilation and Erosion
3.4 A Geometrical Interpretation of Dilation and Erosion Process
3.5 Direct Effect of Edges and Borders on the Erosion and Dilation
3.6 Closing and Opening
3.7 A Historical Review to Definitions and Notations
4 Practical Interpretation of Binary Opening and Closing
5 Morphological Operators in Grayscale Domain
5.1 Basic Morphological Operators in Multivalued Function Domain
5.2 Dilation and Erosion of Multivalued Functions
5.3 Two Forms of Presentation for Dilation and Erosion Formula
6 Opening and Closing of Multivalued Functions
7 Interpretation and Intuitive Understanding of Morphological Filters in Multivalued Function Domain
8 Conclusion
References
Brain Action Inspired Morphological Image Enhancement
1 Introduction
2 Human Visual Perception
3 Visual Illusions
4 Visual Illusions
4.1 Rotating Snakes
5 Mach Bands Illusion
6 Image Enhancement Inspiration from Human Visual Illusion
7 Morphological Image Enhancement Based on Visual Illusion
8 Results and Discussion
9 Summary
References
Path Generation for Software Testing: A Hybrid Approach Using Cuckoo Search and Bat Algorithm
1 Introduction
2 Related Work
3 Motivational Algorithm
3.1 Cuckoo Search Algorithm
3.2 Bat Algorithm [12]4 Proposed Algorithm
5 Path Sequence Generation and Prioritization
6 Analysis of Proposed Algorithm
7 Conclusions and Future Scope
References
An Improved Spider Monkey Optimization for Solving a Convex Economic Dispatch Problem
1 Introduction
2 Related Work
3 Economic Dispatch Problem
3.1 Problem Constraints
3.2 Penalty Function
4 Social Behavior and Foraging of Spider Monkeys
4.1 Fission–Fusion Social Behavior
4.2 Social Organization and Behavior
4.3 Communication of Spider Monkeys
4.4 Characteristic of Spider Monkeys
4.5 The Standard Spider Monkey Optimization Algorithm
4.6 Spider Monkey Optimization Algorithm
5 Multidirectional Search Algorithm
6 The Proposed MDSMO Algorithm
7 Numerical Experiments
7.1 Parameter Setting
7.2 Six-Generator Test System with System Losses
7.3 The General Performance of the Proposed MDSMO with Economic Dispatch Problem
7.4 MDSMO and Other Algorithms
8 Conclusion and Future Work
References
Chance-Constrained Fuzzy Goal Programming with Penalty Functions for Academic Resource Planning in
1 Introduction
2 FGP Problem Formulation
2.1 Membership Function Characterization
2.2 Deterministic Equivalents of Chance Constraints
3 Formulation of Priority Based FGP Model
3.1 Euclidean Distance Function for Priority Structure Selection
4 FGP Model with Penalty Functions
4.1 Penalty Function Description
4.2 Priority Based FGP Model with Penalty Functions
4.3 GA Scheme for FGP Model
5 FGP Formulation of the Problem
5.1 Definitions of Decision Variables and Parameters
5.2 Descriptions of Fuzzy Goals and Constraints
6 A Case Example
6.1 An Illustration for Performance Comparison
7 Conclusions
References
Swarm Intelligence: A Review of Algorithms
1 Introduction
2 Research Methodology
3 Insect-Based Algorithms
3.1 Ant Colony Optimization Algorithm
3.2 Bee-Inspired Algorithms
3.3 Firefly-Based Algorithms
3.4 Glow-Worm-Based Algorithms
4 Animal-Based Algorithms
4.1 Bat-Based Algorithm
4.2 Monkey-Based Algorithm
4.3 Lion-Based Algorithm
4.4 Wolf-Based Algorithm
5 Future Research Directions
6 Conclusions

People also search for Nature Inspired Computing and Optimization Theory and Applications 1st:

nature inspired computing techniques
    
nature theory examples
    
nature computational methods
    
nature inspired optimization algorithms
    
nature inspired computing

Tags: Nature Inspired, Computing, Optimization, Theory, Applications, Srikanta Patnaik