The particle swarm optimization model
The particle swarm optimization (PSO) model is inspired by flocking of birds and the schooling movement of fish. The goal of the PSO model is to find an optimum solution (food source or a place to live) within a dynamic space. The swarm starts at a random location and a random velocity and is based on the collective behavior by exploring and exploiting the search space. The unique feature of PSO is that the agents operate in a formation that optimizes the search and also minimizes the collective effort in converging to an optimum solution. The agents within a swarm that follows the PSO model follow some of the guideline principles:
- Separation: Each individual agent is programmed in a way that it is able to keep a sufficient distance with the flock-mates so that they do not run into each other and at the same time, maintain a separate existence space for itself to be part of a formation in search of an optimum solution. The agent follows the nearest neighbor...
