11.1 Introduction

In this chapter, a brief introduction is given to Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Optimization is the process to find a best optimal solution for the problem under consideration. Particle Swarm Optimization and Ant Colony Optimization achieve finding an optimal solution for the search problems using the social behavior of the living organisms. Particle swarm optimization is a form of swarm intelligence and Ant colony optimization is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. The chapter gives an overview of basic concepts and fucntional operation of Particle Swarm Optimization and Ant Colony Optimization.

11.2 Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.

In past several years, PSO has been successfully applied in many research and application areas. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods. Another reason that PSO is attractive is that there are few parameters to adjust. One version,...