(In)Complete Table of ContentsPart I: OPTIMIZATION THEORY [ Show/Hide ]2 Optimization Problems and Methods
- Basic Ingredients of Optimization Problems
- Optimization Problem Classifications
- Optimality Conditions
- Optimization Method Classes
- General Conditions for Convergence
- Summary
3 Unconstrained Optimization
- Problem Definition
- Optimization Algorithms
- Example Benchmark Problems
- Summary
4 Constrained Optimization
- Definition
- Constraint Handling Methods
- Example Benchmark Problems
- Summary
5 Multi-solution Problems
- Definition
- Niching Algorithm Categories
- Example Benchmark Problems
- Summary
6 Multi-objective Optimization
- Multi-objective Problem
- Pareto-optimality
- Summary
7 Dynamic Optimization Problems
- Definition
- Dynamic Environment Types
- Example Benchmark Problems
- Summary
Part II: EVOLUTIONARY COMPUTATION [ Show/Hide ]8 Introduction to Evolutionary Computation
- General Evolutionary Algorithm
- Representation
- Initial Popolation
- Fitness Function
- Selection
- Reproduction Operators
- Evolutionary Computation versus Classical Optimization
- Summary
9 Evolutionary Computation Paradigms
- Genetic Algorithms
- Genetic Programming
- Evolutionary Programming
- Evolution Strategies
- Differential Evolution
- Cultural Algorithms
- Summary
10 Coevolution
- Competitive Coevolution
- Cooperative Coevolution
- Summary
Part III: PARTICLE SWARM OPTIMIZATION [ Show/Hide ]11 Introduction 12 Basic Particle Swarm Optimization
- Full PSO Model
- Social Network Structures
- Basic Variations
- Basic PSO Parameters
- Performance Measures
- PSO versus EC
- Summary
13 Particle Trajectories
- Convergence
- Surfing the Waves
- Swarm Equilibrium
- Constricted Trajectories
- Unconstricted Trajectories
- Parameter Selection Heuristics
- Summary
14 Convergence Proofs
- Convergence Proof for Basic PSO
- PSO with Guaranteed Local Convergence
- Global Convergence of PSO
- Summary
15 Single-Solution Particle Swarm Optimization
- Social-Based PSO Algorithms
- Hybrid Algorithms
- Sub-swarm-Based PSO
- Memetic PSO Algorithms
- Multi-start PSO Algorithms
- Repelling Methods
- Summary
16 Niching with Particle Swarm Optimization
- Niching Capability of Basic PSO
- Sequential PSO Niching
- Parallel PSO Niching
- Quasi-sequential Niching
- Performance Measures
- Summary
17 Constrained Optimization using Particle Swarm Optimization
- Reject Infeasible Solutions
- Penalty Function Methods
- Convert to Unconstrained Problem
- Repair Methods
- Preserving Feasibility Methods
- Pareto Ranking Methods
- Boundary Constraints
- Applications
- Summary
18 Multi-objective Optimization with Particle Swarms
- Objectives of MOO
- Basic PSO versus MOO
- Aggregation-Based Methods
- Criterion-Based Methods
- Dominance-Based Methods
- Performance Measures
- Summary
19 Dynamic Environments with Particle Swarm Optimization
- Consequences for PSO
- PSO Solutions for Dynamic Environments
- Performance Measurement in Dynamic Environments
- Applications of PSO to Dynamic Problems
- Summary
20 Discrete Particle Swarm Optimization
- Binary PSO
- General Discrete PSO
- Example Applications
- Design of Combinational Circuits
- Summary
21 Particle Swarm Optimization Applications
- Neural Networks
- Game Learning
- Clustering Applications
- Design Applications
- Scheduling and Planning Applications
- Controllers Applications
- Applied Mathematics
- Applications in Power Systems
- Miscellaneous Applications
- Summary
Part IV: ANT ALGORITHMS [ Show/Hide ]22 Introduction 23 Ant Colony Optimization Meta-heuristic
- Foraging Behavior of Ants
- Simple Ant Colony Optimization
- Early Ant Algorithms
- Parameter Settings
- Summary
24 General Frameworks for Ant Colony Optimization Algorithms
- ACO Algorithm Characteristics
- Generic Frameworks
- Summary
25 Ant Colony Optimization Algorithms
- Single Colony ACO Algorithms
- Continuous ACO
- Multiple Colony Algorithms
- Hybrid ACO Algorithms
- Multi-objective Optimization
- Dynamic Optimization Problems
- Parallel ACO Algorithms
- Summary
26 Ant Colony Optimization Applications
- General Requirements
- Ordering Problems
- Assignment Problems
- Subset Problems
- Grouping Problems
- Summary
27 Collective Decision-Making
- Stigmergy
- Artificial Pheromone
- Heterarchy
- Summary
28 Ant Colony Optimization Convergence
- Convergence Proofs and Characteristics
- Convergence Measures
- Summary
29 Cemetery Organization and Brood Care
- Basic Ant Colony Clustering Model
- Generalized Ant Colony Clustering Model
- Minimal Model for Ant Clustering
- Ant Clustering Ensemble
- Hybrid Clustering Approaches
- Ant Clustering Applications
- Summary
30 Division of Labor
- Division of Labor in Insect Colonies
- Task Allocation Based on Response Thresholds
- Adaptive Task Allocation and Specialization
- Summary
Part V: FINAL REMARKS [ Show/Hide ]References Further Reading Appendix A Acronyms
Appendix B Symbols
- Part I -- Optimization Theory
- Part II -- Evolutionary Computation
- Part III -- Particle Swarm Optimization
- Part IV -- Ant Algorithms
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