Ant Colony Optimization Algorithms

The Ant Colony Optimization Algorithm, also known as ACO, is a probabilistic technique for addressing computational problems in the fields of computer science and operations research. These problems can be boiled down to the task of finding good paths through graphs. The behavior of natural ants served as inspiration for the development of multi-agent systems, which are represented by artificial ants. The communication of biological ants through the use of pheromones is frequently the major paradigm that is adopted. Combinations of artificial ants and local search algorithms have become the technique of choice for several optimization tasks involving some kind of graph, such as internet routing and vehicle routing. This is because these combinations are able to find optimal solutions more quickly than traditional methods.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Ant colony optimization algorithms

Chapter 2: Job-shop scheduling

Chapter 3: Open-shop scheduling

Chapter 4: Quadratic assignment problem

Chapter 5: Generalized assignment problem

Chapter 6: Set cover problem

Chapter 7: Partition problem

Chapter 8: Bankruptcy prediction

Chapter 9: Protein-protein interaction

Chapter 10: Protein folding

(II) Answering the public top questions about ant colony optimization algorithms.

(III) Real world examples for the usage of ant colony optimization algorithms in many fields.

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 ant colony optimization algorithms.

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