Optimization Algorithms: AI techniques for design, planning, and control problems

✍ Scribed by Alaa Khamis

• Publisher: Manning
• Year: 2024
• Tongue: English
• Leaves: 669
• Edition: 1
• Category: Library

✦ Synopsis

Solve design, planning, and control problems using modern machine learning and AI techniques.

In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn:
• Machine learning methods for search and optimization problems
• The core concepts of search and optimization
• Deterministic and stochastic optimization techniques
• Graph search algorithms
• Nature-inspired search and optimization algorithms
• Efficient trade-offs between search space exploration and exploitation
• State-of-the-art Python libraries for search and optimization

Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.

About the technology
Search and optimization algorithms are powerful tools that can help practitioners find optimal or near-optimal solutions to a wide range of design, planning and control problems. When you open a route planning app, call for a rideshare, or schedule a hospital appointment, an AI algorithm works behind the scenes to make sure you get an optimized result. This guide reveals the classical and modern algorithms behind these services.

About the book
Optimization Algorithms: AI techniques for design, planning, and control problems explores the AI algorithms that determine the most efficient routes, optimal designs, and solve other logistical issues. Dive into the exciting world of classical problems like the Travelling Salesman Problem and the Knapsack Problem, as well as cutting-edge modern implementations like graph search methods, metaheuristics and machine learning. Discover how to use these algorithms in real-world situations, with in-depth case studies on assembly line balancing, fitness planning, rideshare dispatching, routing and more. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm.

About the reader
For AI practitioners familiar with the Python language.

About the author
Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a sessional lecturer at the University of Toronto. He is also an adjunct professor at Ontario Tech University and Nile University, affiliate member of the Center of Pattern Analysis and Machine Intelligence (CPAMI) at the University of Waterloo, and a former professor of artificial intelligence and robotics.

✦ Table of Contents

brief contents
contents
preface
acknowledgments
about this book
Who should read this book?
How this book is organized: A roadmap
About the code
liveBook discussion forum
about the author
about the cover illustration
Part 1
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
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
Part 2
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
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
Part 3
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
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
Part 4
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
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
Part 5
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
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 versus 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
Appendix A Search and optimization libraries in Python
Appendix B Benchmarks and datasets
Appendix C Exercises and solutions
index

✦ Subjects

Machine Learning; Algorithms; Swarm Intelligence; Genetic Algorithms; Unsupervised Learning; Reinforcement Learning; Supervised Learning; Python; Heuristics; Optimization; Graph Algorithms; Search Algorithms; Dijkstra's Algorithm; Graph Embeddings; A* Algorithm; Metaheuristics; Uniform-cost search; Shortest path algorithms; Graph traversal algorithms; Hill climbing algorithm; Beam search algorithm; Simulated annealing; Sudoku; Tabu Search; Particle swarm optimization