This Research work introduces Particle swarm optimization technique for predicting fire outbreaks in industrial environment. The Particle swarm optimization (PSO) method is a swarm-based heuristic, which mimics the foraging behavior of bird flocks. Two Experiments were conducted, the first Experiment (Exp. 1) using 26 different test simulations was performed, using different fault resistance, a constant population size of 20 and max iteration of 5. It shows that when the fault resistance is between 0.3 ohm – 0.0 ohm, there will be likelihood of danger occurring among all faults at the same time, and none of the faults will be normal. While the second Experiment (Exp. 2) conducted, using 26 different test simulations was performed, using different fault resistance, a constant population size of 100 and max iteration of 50, it proves that when the fault resistance is between 0.35 ohm – 0.0-ohm fault resistance, there will be likelihood of danger occurring among all faults at the same time. Results prove that PSO can be used to predict fire outbreak caused by electrical faults.
Keywords: Particle swarm optimization, fault resistance, fire outbreaks, population size and max iteration., swarm-based heuristic
