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

Have you ever wondered how navigation apps like Google Maps and Apple Maps determine the fastest route from one place to another? Have you been curious about how ride-sharing companies like Uber, Lyft, or DiDi guide their drivers to the best spots to reduce your wait time and their travel distance, making things better for everyone? Or perhaps you've asked yourself how food delivery platforms like Uber Eats suggest food choices to you? Have you considered how last-mile delivery apps map out the fastest routes for parcel deliveries while minimizing empty return trips? Do you ever wonder how emergency responders are dispatched swiftly to incidents? Have you thought about the process behind selecting locations for electric vehicle charging stations? Or how to calculate the best price for a product, plan crop planting, allocate resources, or schedule surgeries efficiently? Optimization Algorithms: AI techniques for design, planning, and control problems dives into the world of artificial intelligence algorithms that tackle these real-world design, planning, and control problems. The book is written for practitioners interested in solving ill-structured search and optimization problems using modern derivative-free algorithms. This book will get you up to speed with the core concepts of search and optimization and endow you with the ability to deal with practical design, planning and control problems.

Without assuming any prior knowledge of search and optimization and with an intermediate knowledge of data structures and Python, this book has been written to take most anyone from never solving search and optimization problems to being a well-rounded search and optimization practitioner able to select, implement and adapt the right solver for the right problem. This book grew out of several courses related to search and optimization taught by me at different universities and training centers in industry.

The book embarks you on a comprehensive journey through a diverse landscape of search and optimization algorithms. We explore deterministic search algorithms that tirelessly traverse problem spaces, seeking optimal solutions through both blind and informed methods. We then climb the peaks and valleys of trajectory-based algorithms, witnessing the power of Simulated Annealing and the ingenious designs of Tabu Search to escape local optima. Continuing on our path, we venture into the realm of evolutionary computing algorithms, witnessing the power of Genetic Algorithms and their variants in solving complex continuous and discrete optimization problems. Along the way, we embark on a fascinating journey with swarm intelligence algorithms, led by Particle Swarm Optimization and a glimpse into other algorithms such as Ant Colony Optimization and Artificial Bee Colony algorithm. Finally, we embrace the realm of machine learning-based methods, where supervised, unsupervised, and reinforcement learning algorithms are used to handle combinatorial optimization problems.

The book is divided into five parts and 12 chapters with more than 600 pages, 114 code listings and several projects and 143 exercises and their solutions.

• Part 1: Deterministic Search Algorithms: Welcome to the first part of our book, where we embark on an exploration of deterministic graph search algorithms. This part consists of four chapters. In Chapter 1, you'll learn the fundamental concepts of search and optimization and understand their real-world significance. You’ll discover how to define optimization problems, differentiate between well-structured and ill-structured ones, and gain insight into the challenges of search algorithms and understand the search dilemma. Chapter 2 dives deeper into the classification of optimization problems. You'll learn how to categorize search and optimization algorithms based on different criteria. Additionally, you’ll learn about heuristics, meta-heuristics, and heuristic search strategies, with a sneak peek at nature-inspired algorithms. In Chapter 3, you'll explore graph search techniques, uncover graph traversal methods, and discover how to use blind search algorithms to find the shortest path between two nodes in a graph, all while solving practical routing problems. In Chapter 4, you'll delve into the concept of informed search. Learn how to solve the minimum spanning tree problem and find the shortest path using informed search algorithms, all while gaining practical problem-solving skills for real-world routing problems. When you're finished with this part of the book, you'll have a solid grasp of the fundamentals of optimization, deterministic graph search algorithms, and practical problem-solving skills applicable to real-world scenarios, setting the stage for the diverse optimization algorithms explored in the following parts of this book.
• Part 2: Trajectory-based Algorithms: Now that you’re armed with a solid foundation in optimization from Part 1, we continue our journey into the realm of optimization algorithms in this part that focuses on Trajectory-based Algorithms. This part, comprised of two chapters, takes your optimization knowledge to the next level. In Chapter 5, you'll learn about trajectory-based optimization algorithms and, specifically, the Simulated Annealing algorithm. Discover how Simulated Annealing can be applied to solve continuous and discrete optimization problems. You'll explore function optimization as an example of continuous optimization, tackle puzzle games like Sudoku as instances of constraint satisfaction problems, delve into permutation problems such as the Traveling Salesman Problem (TSP), and even apply Simulated Annealing to real-world problems, like optimizing delivery routes for semi-trucks. Chapter 6 introduces you to Tabu Search as another trajectory-based optimization algorithm. You'll learn the fundamentals of local search and how Tabu Search builds upon this foundation. This chapter takes you through solving constraint satisfaction problems, continuous optimization problems, routing problems, and balancing assembly lines in manufacturing. By the end of Part 2, you'll have a deep understanding of trajectory-based optimization algorithms and the diverse problem domains they can effectively address. These chapters will equip you with valuable tools to tackle complex optimization problems across a wide range of applications.
• Part 3: Evolutionary Computing Algorithms: As we continue our journey into the world of optimization algorithms, this part introduces you to the fascinating realm of genetic algorithms, a prime example of population-based metaheuristic algorithms. Within the two chapters of this part, you'll delve into the heart of evolutionary computation and unlock the potential of genetic algorithms as versatile tools for solving a wide range of optimization problems. In Chapter 7, you'll learn about population-based optimization algorithms and, more specifically, genetic algorithms. You’ll discover the inner workings of evolutionary computation and gain a comprehensive understanding of the various components that make up genetic algorithms. We'll take a hands-on approach by implementing genetic algorithms in Python, allowing you to apply this powerful technique to practical problem-solving. Chapter 8 takes you further into the world of genetic algorithms, exploring variants that enhance their adaptability to different problem types. You'll delve into Gray-coded genetic algorithms, explore real-valued genetic algorithms and their genetic operators, and understand permutation-based genetic algorithms and their applications. Additionally, you'll discover the concept of multi-objective optimization and learn how to fine-tune genetic algorithms to strike a balance between exploration and exploitation. Through practical examples, you'll see how genetic algorithms can efficiently solve both continuous and discrete optimization problems.
• Part 4: Swarm Intelligence Algorithms: As we advance in our exploration of optimization algorithms, this part immerses you in the world of collective intelligence. Here, you'll discover the power of swarm intelligence algorithms, including Particle Swarm Optimization, Ant Colony Optimization, and Artificial Bee Colony. Through the two chapters in this part, you'll witness how nature-inspired swarming behaviors can be harnessed to find optimal solutions. In Chapter 9, you'll learn about swarm intelligence and dive deep into Particle Swarm Optimization (PSO). Gain an understanding of how swarms of particles collectively explore solution spaces to find optimal answers. You'll explore continuous PSO algorithms, delve into binary PSO for discrete problems, and understand permutation-based PSO for combinatorial optimization. Discover how to adapt PSO to strike a balance between exploration and exploitation, and see how it can efficiently solve both continuous and discrete optimization problems. Chapter 10 broadens your horizons as you explore other swarm intelligence (SI) algorithms. You’ll get familiar with the principles of Ant Colony Optimization (ACO) metaheuristics and understand different variants of ACO that cater to various problem types. Additionally, you’ll explore the world of Artificial Bee Colony (ABC) algorithms and grasp the adaptation aspects that make SI algorithms so versatile. You'll witness how these SI algorithms can be applied to address a wide range of continuous and discrete optimization problems.
• Part 5: Machine Learning-based Methods: In this final part of our journey through optimization algorithms, we delve into the cutting-edge world of machine learning-based methods. In this final part of the book, comprising two chapters, you'll delve into the dynamic world of machine learning techniques and how they can be harnessed to solve complex optimization problems. In Chapter 11, you'll learn how to leverage the power of artificial intelligence, machine learning, and deep learning to tackle optimization problems. We'll start by providing a refresher on these foundational concepts, ensuring you have a strong grounding. You'll then delve into the exciting field of graph machine learning, graph embedding, graph convolutional networks, and attention mechanisms, which are invaluable in solving optimization problems with graph-structured data. Additionally, you'll explore self-organizing maps, uncovering their role in optimization tasks. By the end of this chapter, you'll be well-equipped to apply supervised and unsupervised machine learning techniques to handle optimization problems. Chapter 12 delves into the fascinating realm of reinforcement learning (RL). You'll grasp the fundamental principles underlying RL, understand the concept of a Markov decision process, and delve into actor-critic architecture and proximal policy optimization algorithms. You'll also become acquainted with multi-armed bandits and contextual bandits and learn how these techniques can be applied to solve optimization problems, where decisions lead to optimal outcomes. In this part, you'll bridge the gap between machine learning and optimization, gaining insights into how machine learning can be harnessed to find optimal solutions efficiently. So, let's embark on this final leg of our journey, where the synergy between machine learning and optimization unlocks a new horizon and a shift towards data-driven and intelligent problem-solving.

Throughout this book, wealth of examples and in-depth case studies are provided for both novices and experts. These examples and case studies are thoroughly explained and put into practice with cutting-edge Python libraries dedicated to search and optimization.

My 25 years working as an AI and Robotics professor in the academia and as a technical leader in industry have given me a wealth of experiences to share with you through this book. I hope that the knowledge gained within this book empowers you to tackle intricate challenges, solve real-world problems, and embrace the boundless potential of search and optimization in different domains. The fascinating world of search and optimization algorithms continues to expand and evolve. It is upon us to harness this knowledge, to further our capabilities, to solve the problems of today and shape the future.

For more information, please visit:

• Book website: https://www.manning.com/books/optimization-algorithms
• Book GitHub repo: https://github.com/Optimization-Algorithms-Book/Code-Listings
• Book at Amazon: https://www.amazon.com/Optimization-Algorithms-techniques-planning-problems/dp/163343883X/