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The method of simulated annealing, often known as SA, is a probabilistic approach that can approximate the value of a function's global optimal value. To be more specific, it is a metaheuristic that allows for an approximation of global optimization in a vast search space when dealing with an optimization problem. The global optimal solution can be found using SA for large numbers of local optimal solutions. It is utilized quite frequently in situations in which the search space is discrete. Simulated annealing may be superior to exact algorithms like gradient descent and branch and bound for solving problems where obtaining an approximate global optimum is more important than finding a precise local optimum in a set amount of time. This is the case when finding an approximate global optimum is more important.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Simulated annealing
Chapter 2: Adaptive simulated annealing
Chapter 3: Automatic label placement
Chapter 4: Combinatorial optimization
Chapter 5: Dual-phase evolution
Chapter 6: Graph cuts in computer vision
Chapter 7: Molecular dynamics
Chapter 8: Multidisciplinary design optimization
Chapter 9: Particle swarm optimization
Chapter 10: Quantum annealing
(II) Answering the public top questions about simulated annealing.
(III) Real world examples for the usage of simulated annealing in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of simulated annealing' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of simulated annealing.
