Data Analysis using Particle Swarm Optimization Algorithm - Tai, Zu Jie (2015)
Particle Swarm Optimization (PSO) basically using the method that more tending to social behaviour, for example fish schooling, bird flocking, bees swarming. This is effective since each particle’s solution seems like know each position and its movement. At the end of the swarming, the particle’s solution supposed to confined to one optimal solution. In this paper, some mathematical function and mechanical components with subject to constraint were introduced to perform optimization using PSO. Furthermore, a modified PSO(Accelerated PSO) were also introduced to compare the results with Basic PSO. Some concern parameters for PSO such as the swarm particle(population size), swarm iterations, self and swarm-confidence factor, weight factor also been introduced in this paper, and have been found the best fitness values for each case problem.