AR_Nature-Inspired Optimization Initialization

From the end of this November, the new direction will be demonstrated for the Nature-Inspired Optimization; This chapter is the initialization of the whole book's review. 

The Nature-inspired Optimization, is normally also known as the meta-heuristic algorithms optimization, as its name, comes from the obtaining of the nautral behaviors. Many of them are obtained from the natural creatres' social behavior or their relative habits: for example, one of the famoust evaluation algorithm, the Genetic algorithm (GA) or the obtaining from the ants' social life, the Ant colony algorithm. 

Generally, the evaluation of the algorithms are demonstrated in this book based on the professor Xin-she Yang's research and his general ideas. 

In this book, the chapters of the contents will be demonstrated as below: 

Chapter 1: Introduction of the algorithms

Chapter 2: The analysis of the algorithms 

Chapter 3: The Random walk and Optimization

Chapter 4: Simulated Annealing (SA)

Chapter 5: Genetic Algorithms (GA)

Chapter 6: Differential algorithms (DA)

Chapter 7: Particle Swarm Optimization (PSO)

Chapter 8: Firefly Algorithms (FA) 

Chapter 9: Cuckoo Search (CS)

Chapter 10: Bat Algorithms (BA)

Chapter 11: Flower Pollination Algorithms (FPA)

Chapter 12: The framework of the Self-Tuning Algorithms 

Chapter 13: Deal with the constraints 

Chapter 14: Multi-objective Optimization 

Chapter 15: Other algorithms and Hybrid algorithms 

An example of the Particle Swarm Optimization (PSO)