9 Particle swarm optimization

 

This chapter covers

  • Introducing swarm intelligence
  • Understanding the continuous particle swarm optimization algorithm
  • Understanding binary particle swarm optimization
  • Understanding permutation-based particle swarm optimization
  • Adapting particle swarm optimization for a better trade-off between exploration and exploitation
  • Solving continuous and discrete problems using particle swarm optimization

In the treasure-hunting mission I introduced in chapter 2, suppose you want to collaborate and share information with your friends instead of doing the treasure- hunting alone. However, you do not want to follow a competitive approach in which you only keep better-performing hunters and recruit new hunters to replace poorer-performing ones, like in the genetic algorithm (GA) explained in the previous chapters. You want to adopt a more cooperative approach and keep all the hunters, without replacing any, but you want to give more weight to the better-performing hunters and try to emulate their success. This scenario uses swarm intelligence and corresponds to population-based optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC) algorithms, which will be explained in this fourth part of the book.

9.1 Introducing swarm intelligence

9.2 Continuous PSO

9.2.1 Motion equations

9.2.2 Fitness update

9.2.3 Initialization

9.2.4 Neighborhoods

9.3 Binary PSO

9.4 Permutation-based PSO

9.5 Adaptive PSO

9.5.1 Inertia weight

9.5.2 Cognitive and social components

9.6 Solving the traveling salesman problem

9.7 Neural network training using PSO

Summary