Cultural Algorithms. Recent Advances

Description

Preface Contents About the Author Part I Foundations 1 Introduction to Stochastic Optimisation 1.1 Introduction 1.2 Conventional Optimisation Methods 1.3 Modern Stochastic Methods 1.3.1 Heuristics 1.3.2 Meta-Heuristics 1.3.3 No Free Lunch Theorems 1.3.4 Evolutionary Algorithms (EAs) 1.3.5 Swarm Intelligence (SI) 1.3.6 Socio-inspired Algorithms 1.3.7 Physics Inspired Algorithms 1.3.8 Hyper-Heuristics 1.4 Summary References 2 Introduction to Culture 2.1 Introduction 2.2 What is the Culture? 2.3 Characteristic Features of Culture 2.4 Cultural Learning in Humans and Animals 2.5 Cultural Evolution 2.6 Social Learning Biases 2.7 Who Can Change Cultural Variants? 2.8 Summary References 3 Cultural Algorithms (CAs) 3.1 Introduction 3.2 Overall Framework 3.3 Population Space 3.4 Belief Space 3.4.1 Situational Knowledge 3.4.2 Normative Knowledge 3.4.3 History Knowledge 3.4.4 Topographical Knowledge 3.4.5 Domain Knowledge 3.5 Communication Protocols 3.5.1 Acceptance Function 3.5.2 Influence Function 3.6 Cultural Adjustment 3.7 Algorithmic Steps 3.8 Illustrative Example 3.9 Summary References Part II Application of Cultural Algorithms 4 Applications of Cultural Algorithms in Engineering 4.1 Introduction 4.2 Civil Engineering 4.2.1 Transportation Engineering 4.2.2 Structural Engineering 4.2.3 Environmental Engineering 4.2.4 Urban Engineering 4.2.5 Water Engineering 4.3 Mechanical Engineering 4.3.1 Damage Detection 4.3.2 Optimum Design 4.3.3 Composite Structures 4.3.4 Optimal Vibration Control 4.3.5 Vehicle Safety 4.3.6 Flight Control 4.3.7 Gas Turbine Operation 4.3.8 Renewable Energy 4.4 Chemical Engineering 4.4.1 Penicillin Production 4.4.2 Ammonia Synthesis 4.4.3 Diesel Blending 4.4.4 Wax Precipitation 4.5 Electrical Engineering 4.5.1 Economic/Environmental Dispatch of Power Systems 4.5.2 Power Distribution Systems 4.5.3 Phased Array Radars 4.5.4 Wireless Sensor Networks 4.5.5 Signal Processing 4.5.6 Maintenance Scheduling 4.5.7 Digital Watermarking 4.5.8 Power System Stabiliser Design 4.5.9 Optimal Reactive Power Dispatch 4.5.10 Optimal Operation of Cascade Hydropower Station 4.5.11 Optimal Design of Passive Power Filters 4.5.12 Hydrothermal Scheduling 4.5.13 Hydroproduction Scheduling 4.5.14 Multi-robot Tasks Allocation 4.6 Industrial Engineering 4.6.1 Supply Chain Management 4.6.2 Industrial Automation 4.7 Summary References 5 Applications of Cultural Algorithms in Different Branches of Science 5.1 Introduction 5.2 Computer Science 5.2.1 Function Optimisation 5.2.2 Job-Shop Scheduling 5.2.3 Facility Location 5.2.4 Set Covering 5.2.5 Semantic Network 5.2.6 Program Understanding 5.2.7 Timetabling 5.2.8 Intelligent Logistics 5.2.9 Artificial Neural Networks 5.2.10 Multi-aircraft Target Allocation 5.2.11 Travelling Salesman Problem 5.2.12 Data Mining 5.2.13 Cloud Computing 5.2.14 Fog Computing 5.2.15 Online Marketing 5.2.16 Genetic Programming 5.2.17 Games 5.2.18 Software Testing 5.2.19 Image Segmentation 5.2.20 Image Matching 5.2.21 Sonar Systems 5.2.22 Gesture Recognition 5.3 Social Science 5.3.1 Anthropology 5.3.2 Virtual Enterprise 5.4 Archaeology 5.5 Physics 5.6 Others 5.7 Summary References Part III Variants of Cultural Algorithms 6 Hybrid Schemes for Cultural Algorithms 6.1 Introduction 6.2 Hybrid CA and PSO 6.2.1 Standard PSO 6.2.2 Multi-objective CA with Multiple PSO-Based Sub-Swarms 6.2.3 Hybrid Gaussian PSO and CA 6.2.4 Modified PSO Update Formula Based on CA 6.3 Hybrid CA and DE 6.3.1 Standard DE 6.3.2 CA with DE-Based Population Space 6.3.3 Hybrid CA and DE with Random Knowledge Influence 6.3.4 High-Level Teamwork Hybrid Model of CA and DE 6.4 Hybrid CA and HS 6.4.1 Standard HS 6.4.2 Hybrid CA and HS 6.5 Hybrid CAs with GAs 6.6 Hybrid CAs and Local Search Techniques 6.6.1 Hybrid CA and TS 6.6.2 Niche CA 6.6.3 Balanced CA with Local Search 6.7 Other Hybridisation Schemes 6.7.1 Hybrid CA and FA 6.7.2 Hybrid CA and IWO 6.7.3 Hybrid CA and SFLA 6.7.4 Hybrid CA and FECO 6.7.5 Hybrid CA and EHO 6.8 Hybrid CA and Evolutionary Programming (EP) 6.9 Summary References 7 Extended Cultural Algorithms 7.1 Introduction 7.2 New Influence Functions 7.2.1 Modified Influence Function 7.2.2 Gravity-Based Influence Function 7.2.3 The Truncated Geometric Distribution-Based Influence Function 7.2.4 Cuckoo Search-Based Influence 7.3 Dynamic Acceptance 7.3.1 Similarity-Based Acceptance Function 7.3.2 Fitness-Based Acceptance Function 7.4 Extended Domain Knowledge 7.5 Communication Topologies for CAs 7.5.1 Social Fabric 7.6 Multi-objective CAs 7.7 Fuzzy CAs 7.7.1 Fuzzy Acceptance 7.7.2 Fuzzy Influence 7.7.3 Fuzzy Update 7.7.4 Fuzzy Clustering 7.8 Chaotic CAs 7.8.1 Belief Space with Chaotic Search 7.8.2 Chaotic Influence 7.8.3 Chaotic Mutation 7.9 Summary References 8 Multi-population Variants of Cultural Algorithms 8.1 Introduction 8.2 Multi-population CAs with Knowledge Migration 8.2.1 Multi-population CA Adopting Knowledge Migration 8.2.2 Multi-population Cooperative CA 8.2.3 Multi-population CA with Fuzzy Clustering 8.3 Multi-population CAs with Shared Global Belief Space 8.3.1 Clan-Based CAs 8.4 Multi-population CA with Different Knowledge-Based Sub-populations 8.5 Multi-population CA with Dynamic Dimension Decomposition 8.6 Summary References 9 Application of Cultural Algorithms to Structural Optimisation 9.1 Introduction 9.2 Algorithms 9.3 Optimum Design of Barrel Vault Structure Under Static Constraints 9.4 Size and Shape Optimisation of Lamella Dome Structure Under Frequency Constraints 9.5 Summary References Appendix MATLAB Code of CAs -4pt- Glossary Bibliography Index