Optimization Algorithms

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 Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems.In Optimization Algorithms: AI techniques for design, planning, and control problems you 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 optimizationInside 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 technologyEvery 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 bookOptimization Algorithms explains 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 exploitationAbout the readerRequires intermediate Python and machine learning skills.About the authorDr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto.The technical editor on this book was Frances Buontempo.Table of ContentsPART 11 Introduction to search and optimization2 A deeper look at search and optimization3 Blind search algorithms4 Informed search algorithmsPART 25 Simulated annealing6 Tabu searchPART 37 Genetic algorithms8 Genetic algorithm variantsPART 49 Particle swarm optimization10 Other swarm intelligence algorithms to explorePART 511 Supervised and unsupervised learning12 Reinforcement learningincluded with the eBook only:Appendix AAppendix BAppendix C

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