Nature-Inspired Computation in Navigation and Routing Problems: Algorithms, Methods and Applications (Springer Tracts in Nature-Inspired Computing) 9789811518416, 9789811518423, 9811518416

Table of contents :
Preface
Contents
Editors and Contributors
1 Navigation, Routing and Nature-Inspired Optimization
1 Introduction
2 Navigation in Animals
3 Navigation, Routing and Optimization
3.1 Optimization
3.2 Travelling Salesman Problem
3.3 Routing Problems
4 Nature-Inspired Algorithms for Optimization
4.1 Deterministic or Stochastic
4.2 Genetic Algorithms
4.3 Ant Colony Optimization
4.4 Particle Swarm Optimization
4.5 Firefly Algorithm
4.6 Cuckoo Search
4.7 Bat Algorithm
4.8 Flower Pollination Algorithm
4.9 Other Algorithms
5 Algorithmic Characteristics
5.1 Characteristics
5.2 Discretization and Solution Representations
6 Conclusions
References
2 Navigation and Navigation Algorithms
1 Navigation Introduction
1.1 Navigation Origin
1.2 Navigation Definition
2 Development of Navigation
2.1 Initial Germination Stage
2.2 Low-Speed Development Stage
2.3 Prosperity and Active Stage
2.4 Blooming Stage
3 Navigation Algorithms
3.1 Ecosystem Simulation Algorithm
3.2 Swarm Intelligence Algorithm
3.3 Evolutionary Algorithm
3.4 Artificial Intelligence Algorithm
4 Development Tendency of Navigation Algorithms
4.1 Development Status of Navigation Algorithms
4.2 Development Tendency of Navigation Algorithm
5 Application of Navigation Algorithm
5.1 Application of Aviation Navigation Algorithm
5.2 Application of Land Navigation Algorithm
5.3 Application of Sea Surface Navigation Algorithm
5.4 Application of Underwater Navigation Algorithm
References
3 Is the Vehicle Routing Problem Dead? An Overview Through Bioinspired Perspective and a Prospect of Opportunities
1 Introduction
2 Problem Statement
3 Recent Advances in Vehicle Routing Problem
3.1 Vehicle Routing Problem and Genetic Algorithms
3.2 Vehicle Routing Problem and Tabu Search
3.3 Vehicle Routing Problem and Simulated Annealing
3.4 Vehicle Routing Problem and Particle Swarm Optimization
3.5 Vehicle Routing Problem and Artificial Bee Colony
3.6 Vehicle Routing Problem and Ant Colony Optimization
3.7 Vehicle Routing Problem and Cuckoo Search
3.8 Vehicle Routing Problem and Imperialist Competitive Algorithm
3.9 Vehicle Routing Problem and Bat Algorithm
3.10 Vehicle Routing Problem and Firefly Algorithm
3.11 Vehicle Routing Problem and Other Nature-Inspired Metaheuristics
4 Challenges and Research Opportunities
5 Conclusions
References
4 Review of Tour Generation for Solving Traveling Salesman Problems
1 Introduction
2 Traveling Salesman Problem (TSP)
2.1 History
2.2 Definitions
2.3 Applications
3 Best Tour Generation
3.1 Tour Construction
3.2 Tour Improvement
4 TSP Solution Space
4.1 Search Space Model
4.2 Constraints
5 Search Methods
6 Example: Discrete Cuckoo Search
6.1 First Layer: Construct a Solution
6.2 Second Layer:Improving the Solution
6.3 Third Layer: Local Optimum Escaping Methods
6.4 Fourth Layer: Discrete Cuckoo Search
7 Conclusion
References
5 Flow Shop Scheduling By Nature-Inspired Algorithms
1 Introduction
2 Problem Definition
3 Literature Review
3.1 Ant Colony Optimization (ACO)
3.2 African Wild Dog Algorithm
3.3 Artificial Bee Colony (ABC)
3.4 Bacterial Foraging Optimization Algorithm (BFOA)
3.5 Bat Algorithm (BA)
3.6 Cuckoo Search (CS)
3.7 Crow Search Algorithm (CSA)
3.8 Firefly Algorithm (FA)
3.9 Flower Pollination Algorithm (FPA)
3.10 Fruit Fly Optimization Algorithm (FFO)
3.11 Gray Wolf Optimization (GWO) Algorithm
3.12 Invasive Weed Optimization (IWO) Algorithm
3.13 Migrating Birds Optimization (MBO) Algorithm
3.14 Monkey Search Algorithm
3.15 Particle Swarm Optimization (PSO)
3.16 Rhinoceros Search Algorithm (RSA)
3.17 Sheep Flock Heredity Algorithm (SFHA)
3.18 Shuffled Frog Leaping Algorithm (SFLA)
3.19 Water Wave Optimization (WWO) Algorithm
3.20 Whale Optimization Algorithm (WOA)
4 Future Direction of Research
5 Conclusions
References
6 Mobile Robot Path Planning Using a Flower Pollination Algorithm-Based Approach
1 Introduction
1.1 Multi-robot Path Planning Approach
1.2 Soft Computing-Based Approaches
1.3 Challenges in the Application of Artificial Intelligent Approaches
2 Flower Pollination Algorithm
2.1 Basic Principle
2.2 Proposed Approach for Robot Path Planning
3 Results and Discussions
4 Conclusions for the Book Chapter
References
7 Smartphone Indoor Localization Using Bio-inspired Modeling
1 Introduction
2 Background
2.1 Indoor Localization with Smartphones
2.2 Bio-inspired Computing: An Overview
3 Literature Review
3.1 Localization Using Bio-inspired Techniques
3.2 Smartphone Indoor Navigation Using Bio-inspired Techniques
4 Indoor Localization Using Artificial Neural Networks
4.1 System Model
4.2 Research Goal
4.3 Radiomap Modeling Using Artificial Neural Networks
5 Experimental Evaluation
5.1 Datasets and Evaluation Metrics
5.2 Evaluation Results
6 Conclusions and Future Challenges
References
8 A New Obstacle Avoidance Technique Based on the Directional Bat Algorithm for Path Planning and Navigation of Autonomous Overhead Traveling Cranes
1 Introduction
2 BA, dBA and Variants
2.1 The Standard Bat Algorithm
2.2 Recent Advance in Improving the Bat Algorithm
2.3 The Directional Bat Algorithm
3 The Proposed Strategy for OTC Autonomous Path Planning
4 Simulation, Results and Discussions
5 Conclusions
Appendix
References
9 Natural Heuristic Methods for Underwater Vehicle Path Planning
1 Path Planning of Underwater Vehicle
1.1 Path Planning
1.2 Objective Optimization
1.3 Main Processes
1.4 The Key to the Problems
2 Characteristics of Underwater Path Planning
2.1 Safe Navigation Factors
2.2 Hidden Navigation Factors
2.3 Marine Environmental Factors
3 Intelligent Path Planning Algorithm
3.1 Neural Network Method
3.2 Fuzzy Logic Method
3.3 Genetic Algorithm
3.4 Ant Colony Algorithm
4 Firefly Algorithm
4.1 Bionics Principle
4.2 Algorithm Description
4.3 Algorithm Flow
4.4 Performance Analysis
4.5 Algorithm Improvement
5 Route Planning Based on Firefly Algorithm
5.1 Environmental Modeling
5.2 Route Expression
5.3 Evaluation Function
5.4 Process Design
5.5 Simulation
References