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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 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.
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.
Optimization 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 inside
- Graph search algorithms
- Metaheuristic algorithms
- Machine learning methods
- State-of-the-art Python libraries for optimization
- Efficient trade-offs between search space exploration and exploitation
Table of Contents
1 Part 1. Deterministic search algorithms
2 Introduction to search and optimization
3 Going from toy problems to the real world
4 Basic ingredients of optimization problems
5 Well-structured problems vs. ill-structured problems
6 Search algorithms and the search dilemma
7 Summary
8 A deeper look at search and optimization
9 Classifying search and optimization algorithms
10 Heuristics and metaheuristics
11 Nature-inspired algorithms
12 Summary
13 Blind search algorithms
14 Graph search
15 Graph traversal algorithms
16 Shortest path algorithms
17 Applying blind search to the routing problem
18 Summary
19 Informed search algorithms
20 Minimum spanning tree algorithms
21 Shortest path algorithms
22 Applying informed search to a routing problem
23 Summary
24 Part 2. Trajectory-based algorithms
25 Simulated annealing
26 The simulated annealing algorithm
27 Function optimization
28 Solving Sudoku
29 Solving TSP
30 Solving a delivery semi-truck routing problem
31 Summary
32 Tabu search
33 Tabu search algorithm
34 Solving constraint satisfaction problems
35 Solving continuous problems
36 Solving TSP and routing problems
37 Assembly line balancing problem
38 Summary
39 Part 3. Evolutionary computing algorithms
40 Genetic algorithms
41 Introducing evolutionary computation
42 Genetic algorithm building blocks
43 Implementing genetic algorithms in Python
44 Summary
45 Genetic algorithm variants
46 Real-valued GA
47 Permutation-based GA
48 Multi-objective optimization
49 Adaptive GA
50 Solving the traveling salesman problem
51 PID tuning problem
52 Political districting problem
53 Summary
54 Part 4. Swarm intelligence algorithms
55 Particle swarm optimization
56 Continuous PSO
57 Binary PSO
58 Permutation-based PSO
59 Adaptive PSO
60 Solving the traveling salesman problem
61 Neural network training using PSO
62 Summary
63 Other swarm intelligence algorithms to explore
64 ACO metaheuristics
65 ACO variants
66 From hive to optimization
67 Exploring the artificial bee colony algorithm
68 Summary
69 Part 5. Machine learning-based methods
70 Supervised and unsupervised learning
71 Demystifying machine learning
72 Machine learning with graphs
73 Self-organizing maps
74 Machine learning for optimization problems
75 Solving function optimization using supervised machine learning
76 Solving TSP using supervised graph machine learning
77 Solving TSP using unsupervised machine learning
78 Finding a convex hull
79 Summary
80 Reinforcement learning
81 Optimization with reinforcement learning
82 Balancing CartPole using A2C and PPO
83 Autonomous coordination in mobile networks using PPO
84 Solving the truck selection problem using contextual bandits
85 Journey s end A final reflection
86 Summary
87 Search and optimization libraries in Python
88 Mathematical programming solvers
89 Graph and mapping libraries
90 Metaheuristics optimization libraries
91 Machine learning libraries
92 Projects
93 Benchmarks and datasets
94 Combinatorial optimization benchmark datasets
95 Geospatial datasets
96 Machine learning datasets
97 Data folder
98 Exercises and solutions
99 Blind search algorithms
100 Informed search algorithms
101 Simulated annealing
102 Tabu search
103 Genetic algorithm
104 Genetic algorithm variants
105 Particle swarm optimization
106 Other swarm intelligence algorithms to explore
107 Supervised and unsupervised learning
108 Reinforcement learning
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