Optimization Algorithms

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 optimization
• Deterministic and stochastic optimization techniques
• Graph search algorithms
• Trajectory-based optimization algorithms
• Evolutionary computing algorithms
• Swarm intelligence algorithms
• Machine learning methods for search and optimization problems
• Efficient trade-offs between search space exploration and exploitation
• State-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.