2 Optimization Problems and Methods

1. Basic Ingredients of Optimization Problems
2. Optimization Problem Classifications
3. Optimality Conditions
4. Optimization Method Classes
5. General Conditions for Convergence
6. Summary

3 Unconstrained Optimization

1. Problem Definition
2. Optimization Algorithms
3. Example Benchmark Problems
4. Summary

4 Constrained Optimization

1. Definition
2. Constraint Handling Methods
3. Example Benchmark Problems
4. Summary

5 Multi-solution Problems

1. Definition
2. Niching Algorithm Categories
3. Example Benchmark Problems
4. Summary

6 Multi-objective Optimization

1. Multi-objective Problem
2. Pareto-optimality
3. Summary

7 Dynamic Optimization Problems

1. Definition
2. Dynamic Environment Types
3. Example Benchmark Problems
4. Summary

8 Introduction to Evolutionary Computation

1. General Evolutionary Algorithm
2. Representation
3. Initial Popolation
4. Fitness Function
5. Selection
6. Reproduction Operators
7. Evolutionary Computation versus Classical Optimization
8. Summary

9 Evolutionary Computation Paradigms

1. Genetic Algorithms
2. Genetic Programming
3. Evolutionary Programming
4. Evolution Strategies
5. Differential Evolution
6. Cultural Algorithms
7. Summary

10 Coevolution

1. Competitive Coevolution
2. Cooperative Coevolution
3. Summary

11 Introduction

12 Basic Particle Swarm Optimization

1. Full PSO Model
2. Social Network Structures
3. Basic Variations
4. Basic PSO Parameters
5. Performance Measures
6. PSO versus EC
7. Summary

13 Particle Trajectories

1. Convergence
2. Surfing the Waves
3. Swarm Equilibrium
4. Constricted Trajectories
5. Unconstricted Trajectories
6. Parameter Selection Heuristics
7. Summary

14 Convergence Proofs

1. Convergence Proof for Basic PSO
2. PSO with Guaranteed Local Convergence
3. Global Convergence of PSO
4. Summary

15 Single-Solution Particle Swarm Optimization

1. Social-Based PSO Algorithms
2. Hybrid Algorithms
3. Sub-swarm-Based PSO
4. Memetic PSO Algorithms
5. Multi-start PSO Algorithms
6. Repelling Methods
7. Summary

16 Niching with Particle Swarm Optimization

1. Niching Capability of Basic PSO
2. Sequential PSO Niching
3. Parallel PSO Niching
4. Quasi-sequential Niching
5. Performance Measures
6. Summary

17 Constrained Optimization using Particle Swarm Optimization

1. Reject Infeasible Solutions
2. Penalty Function Methods
3. Convert to Unconstrained Problem
4. Repair Methods
5. Preserving Feasibility Methods
6. Pareto Ranking Methods
7. Boundary Constraints
8. Applications
9. Summary

18 Multi-objective Optimization with Particle Swarms

1. Objectives of MOO
2. Basic PSO versus MOO
3. Aggregation-Based Methods
4. Criterion-Based Methods
5. Dominance-Based Methods
6. Performance Measures
7. Summary

19 Dynamic Environments with Particle Swarm Optimization

1. Consequences for PSO
2. PSO Solutions for Dynamic Environments
3. Performance Measurement in Dynamic Environments
4. Applications of PSO to Dynamic Problems
5. Summary

20 Discrete Particle Swarm Optimization

1. Binary PSO
2. General Discrete PSO
3. Example Applications
4. Design of Combinational Circuits
5. Summary

21 Particle Swarm Optimization Applications

1. Neural Networks
2. Game Learning
3. Clustering Applications
4. Design Applications
5. Scheduling and Planning Applications
6. Controllers Applications
7. Applied Mathematics
8. Applications in Power Systems
9. Miscellaneous Applications
10. Summary

22 Introduction

23 Ant Colony Optimization Meta-heuristic

1. Foraging Behavior of Ants
2. Simple Ant Colony Optimization
3. Early Ant Algorithms
4. Parameter Settings
5. Summary

24 General Frameworks for Ant Colony Optimization Algorithms

1. ACO Algorithm Characteristics
2. Generic Frameworks
3. Summary

25 Ant Colony Optimization Algorithms

1. Single Colony ACO Algorithms
2. Continuous ACO
3. Multiple Colony Algorithms
4. Hybrid ACO Algorithms
5. Multi-objective Optimization
6. Dynamic Optimization Problems
7. Parallel ACO Algorithms
8. Summary

26 Ant Colony Optimization Applications

1. General Requirements
2. Ordering Problems
3. Assignment Problems
4. Subset Problems
5. Grouping Problems
6. Summary

27 Collective Decision-Making

1. Stigmergy
2. Artificial Pheromone
3. Heterarchy
4. Summary

28 Ant Colony Optimization Convergence

1. Convergence Proofs and Characteristics
2. Convergence Measures
3. Summary

29 Cemetery Organization and Brood Care

1. Basic Ant Colony Clustering Model
2. Generalized Ant Colony Clustering Model
3. Minimal Model for Ant Clustering
4. Ant Clustering Ensemble
5. Hybrid Clustering Approaches
6. Ant Clustering Applications
7. Summary

30 Division of Labor

1. Division of Labor in Insect Colonies
2. Task Allocation Based on Response Thresholds
3. Adaptive Task Allocation and Specialization
4. Summary

References

Further Reading

Appendix A Acronyms

Appendix B Symbols

1. Part I -- Optimization Theory
2. Part II -- Evolutionary Computation
3. Part III -- Particle Swarm Optimization
4. Part IV -- Ant Algorithms