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Optimization Algorithms

by Alaa Khamis

Algorithm Data Structures

Book Details

Book Title

Author

Alaa Khamis

Publisher

Manning Publications

Publication Date

2024

ISBN

9781633438835

Number of Pages

669

Language

English

Format

PDF

File Size

8.6MB

Subject

Computers>Algorithms and Data Structures

Part 1. Deterministic search algorithms
Chapter 1: Introduction to search and optimization
1.1 Why care about search and optimization?
1.2 Going from toy problems to the real world
1.3 Basic ingredients of optimization problems
1.3.1 Decision variables
1.3.2 Objective functions
1.3.3 Constraints
1.4 Well-structured problems vs. ill-structured problems
1.4.1 Well-structured problems
1.4.2 Ill-structured problems
1.4.3 WSP, but ISP in practice
1.5 Search algorithms and the search dilemma
Summary
Chapter 2: A deeper look at search and optimization
2.1 Classifying optimization problems
2.1.1 Number and type of decision variables
2.1.2 Landscape and number of objective functions
2.1.3 Constraints
2.1.4 Linearity of objective functions and constraints
2.1.5 Expected quality and permissible time for the solution
2.2 Classifying search and optimization algorithms
2.3 Heuristics and metaheuristics
2.4 Nature-inspired algorithms
Summary
Chapter 3: Blind search algorithms
3.1 Introduction to graphs
3.2 Graph search
3.3 Graph traversal algorithms
3.3.1 Breadth-first search
3.3.2 Depth-first search
3.4 Shortest path algorithms
3.4.1 Dijkstra’s search
3.4.2 Uniform-cost search (UCS)
3.4.3 Bidirectional Dijkstra's search
3.5 Applying blind search to the routing problem
Summary
Chapter 4: Informed search algorithms
4.1 Introducing informed search
4.2 Minimum spanning tree algorithms
4.3 Shortest path algorithms
4.3.1 Hill climbing algorithm
4.3.2 Beam search algorithm
4.3.3 A* search algorithm
4.3.4 Hierarchical approaches
4.4 Applying informed search to a routing problem
4.4.1 Hill climbing for routing
4.4.2 Beam search for routing
4.4.3 A* for routing
4.4.4 Contraction hierarchies for routing
Summary
Part 2. Trajectory-based algorithms
Chapter 5: Simulated annealing
5.1 Introducing trajectory-based optimization
5.2 The simulated annealing algorithm
5.2.1 Physical annealing
5.2.2 SA pseudocode
5.2.3 Acceptance probability
5.2.4 The annealing process
5.2.5 Adaptation in SA
5.3 Function optimization
5.4 Solving Sudoku
5.5 Solving TSP
5.6 Solving a delivery semi-truck routing problem
Summary
Chapter 6: Tabu search
6.1 Local search
6.2 Tabu search algorithm
6.2.1 Memory structure
6.2.2 Aspiration criteria
6.2.3 Adaptation in TS
6.3 Solving constraint satisfaction problems
6.4 Solving continuous problems
6.5 Solving TSP and routing problems
6.6 Assembly line balancing problem
Summary
Part 3. Evolutionary computing algorithms
Chapter 7: Genetic algorithms
7.1 Population-based metaheuristic algorithms
7.2 Introducing evolutionary computation
7.2.1 A brief recap of biology fundamentals
7.2.2 The theory of evolution
7.2.3 Evolutionary computation
7.3 Genetic algorithm building blocks
7.3.1 Fitness function
7.3.2 Representation schemes
7.3.3 Selection operators
7.3.4 Reproduction operators
7.3.5 Survivor selection
7.4 Implementing genetic algorithms in Python
Summary
Chapter 8: Genetic algorithm variants
8.1 Gray-coded GA
8.2 Real-valued GA
8.2.1 Crossover methods
8.2.2 Mutation methods
8.3 Permutation-based GA
8.3.1 Crossover methods
8.3.2 Mutation methods
8.4 Multi-objective optimization
8.5 Adaptive GA
8.6 Solving the traveling salesman problem
8.7 PID tuning problem
8.8 Political districting problem
Summary
Part 4. Swarm intelligence algorithms
Chapter 9: Particle swarm optimization
9.1 Introducing swarm intelligence
9.2 Continuous PSO
9.2.1 Motion equations
9.2.2 Fitness update
9.2.3 Initialization
9.2.4 Neighborhoods
9.3 Binary PSO
9.4 Permutation-based PSO
9.5 Adaptive PSO
9.5.1 Inertia weight
9.5.2 Cognitive and social components
9.6 Solving the traveling salesman problem
9.7 Neural network training using PSO
Summary
Chapter 10: Other swarm intelligence algorithms to explore
10.1 Nature’s tiny problem-solvers
10.2 ACO metaheuristics
10.3 ACO variants
10.3.1 Simple ACO
10.3.2 Ant system
10.3.3 Ant colony system
10.3.4 Max–min ant system
10.3.5 Solving open TSP with ACO
10.4 From hive to optimization
10.5 Exploring the artificial bee colony algorithm
Summary
Part 5. Machine learning-based methods
Chapter 11: Supervised and unsupervised learning
11.1 A day in the life of AI-empowered daily routines
11.2 Demystifying machine learning
11.3 Machine learning with graphs
11.3.1 Graph embedding
11.3.2 Attention mechanisms
11.3.3 Pointer networks
11.4 Self-organizing maps
11.5 Machine learning for optimization problems
11.6 Solving function optimization using supervised machine learning
11.7 Solving TSP using supervised graph machine learning
11.8 Solving TSP using unsupervised machine learning
11.9 Finding a convex hull
Summary
Chapter 12: Reinforcement learning
12.1 Demystifying reinforcement learning
12.1.1 Markov decision process (MDP)
12.1.2 From MDP to reinforcement learning
12.1.3 Model-based vs. model-free RL
12.1.4 Actor-critic methods
12.1.5 Proximal policy optimization
12.1.6 Multi-armed bandit (MAB)
12.2 Optimization with reinforcement learning
12.3 Balancing CartPole using A2C and PPO
12.4 Autonomous coordination in MOBIle networks using PPO
12.5 Solving the truck selection problem using contextual bandits
12.6 Journey’s end: A final reflection
Summary
Appendix A. Search and optimization libraries in Python
A.1 Setting up the Python environment
A.1.1 Using a Python distribution
A.1.2 Installing Jupyter Notebook and JupyterLab
A.1.3 Cloning the book’s repository
A.2 Mathematical programming solvers
A.2.1 SciPy
A.2.2 PuLP
A.2.3 Other mathematical programming solvers
A.3 Graph and mapping libraries
A.3.1 NetworkX
A.3.2 OSMnx
A.3.3 GeoPandas
A.3.4 contextily
A.3.5 Folium
A.3.6 Other libraries and tools
A.4 Metaheuristics optimization libraries
A.4.1 PySwarms
A.4.2 Scikit-opt
A.4.3 NetworkX
A.4.4 Distributed evolutionary algorithms in Python (DEAP)
A.4.5 OR-Tools
A.4.6 Other libraries
A.5 Machine learning libraries
A.5.1 node2vec
A.5.2 DeepWalk
A.5.3 PyG
A.5.4 OpenAI Gym
A.5.5 Flow
A.5.6 Other libraries
A.6 Projects
Appendix B. Benchmarks and datasets
B.1 Optimization test functions
B.2 Combinatorial optimization benchmark datasets
B.3 Geospatial datasets
B.4 Machine learning datasets
B.5 Data folder
Appendix C. Exercises and solutions
C.1 Chapter 2: A deeper look at search and optimization
C.1.1 Exercises
C.1.2 Solutions
C.2 Chapter 3: Blind search algorithms
C.2.1 Exercises
C.2.2 Solutions
C.3 Chapter 4: Informed search algorithms
C.3.1 Exercises
C.3.2 Solutions
C.4 Chapter 5: Simulated annealing
C.4.1 Exercises
C.4.2 Solutions
C.5 Chapter 6: Tabu search
C.5.1 Exercises
C.5.2 Solutions
C.6 Chapter 7: Genetic algorithm
C.6.1 Exercises
C.6.2 Solutions
C.7 Chapter 8: Genetic algorithm variants
C.7.1 Exercises
C.7.2 Solutions
C.8 Chapter 9: Particle swarm optimization
C.8.1 Exercises
C.8.2 Solutions
C.9 Chapter 10: Other swarm intelligence algorithms to explore
C.9.1 Exercises
C.9.2 Solutions
C.10 Chapter 11: Supervised and unsupervised learning
C.10.1 Exercises
C.10.2 Solutions
C.11 Chapter 12: Reinforcement learning
C.11.1 Exercises
C.11.2 Solutions
References
Index

Optimization Algorithms

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