Nature-inspired computing and optimization : theory and applications

The text provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimisation. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications.

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Bibliographic Details
Main Authors Patnaik, Srikanta, Yang, Xin-She, 中松, 和巳
Format eBook Book
LanguageEnglish
Published Cham Springer 2017
Springer International Publishing AG
Springer International Publishing
Edition1
SeriesModeling and Optimization in Science and Technologies
Subjects
Artificial Intelligence
Computational Intelligence
Engineering
Engineering Economics, Organization, Logistics, Marketing
Engineering economy
Natural computation
Optimization
Simulation and Modeling
Online AccessGet full text
ISBN9783319509198
3319509195
ISSN2196-7326
2196-7334
DOI10.1007/978-3-319-50920-4

Cover

Table of Contents:
  • 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 Healthy Nutritional Meals for Older Adults -- 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 Recommendations -- 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 Computing -- 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
  • 7 Interpretation and Intuitive Understanding of Morphological Filters in Multivalued Function Domain
  • Intro -- Preface -- Contents -- Contributors -- Members of Review Board -- The Nature of Nature: Why Nature-Inspired Algorithms Work -- 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 Environments -- 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
  • 4.2 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