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

Solve design, planning, and control problems using modern AI techniques.
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 Algorithmsintroduces the AI algorithms that can solve these complex and poorly-structured problems.
InOptimization Algorithms: AI techniques for design, planning, and control problemsyou will learn:
The core concepts of search and optimizationDeterministic and stochastic optimization techniquesGraph search algorithmsTrajectory-based optimization algorithmsEvolutionary computing algorithmsSwarm intelligence algorithmsMachine learning methods for search and optimization problemsEfficient trade-offs between search space exploration and exploitationState-of-the-art Python libraries for search and optimization
Inside this comprehensive guide, you'll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic 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. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm.
About the technology
Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how.
About the book
Optimization Algorithmsexplains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges.
What's insideGraph search algorithmsMetaheuristic algorithmsMachine learning methodsState-of-the-art Python libraries for optimizationEfficient trade-offs between search space exploration and exploitation
About the reader
Requires intermediate Python and machine learning skills.
About the author
Dr.Alaa Khamisis an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto.
The technical editor on this book wasFrances Buontempo.
Table of Contents
PART 1
1 Introduction to search and optimization
2 A deeper look at search and optimization
3 Blind search algorithms
4 Informed search algorithms
PART 2
5 Simulated annealing
6 Tabu search
PART 3
7 Genetic algorithms
8 Genetic algorithm variants
PART 4
9 Particle swarm optimization
10 Other swarm intelligence algorithms to explore
PART 5
11 Supervised and unsupervised learning
12 Reinforcement learning
Appendix A
Appendix B
Appendix C



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