Particle Swarm Optimization: Velocity Initialization

Since its birth in 1995, particle swarm optimization (PSO) has been well studied and successfully applied. While a better understanding of PSO and particle behaviors have been obtained through theoretical and empirical analysis, some issues about the beavior of particles remain unanswered. One such issue is how velocities should be initialized. Though zero initial velocities have been advocated, a popular initialization strategy is to set initial weights to random values within the domain of the optimization problem. This article first illustrates that particles tend to leave the boundaries of the search space irrespective of the initialization approach, resulting in wasted search effort. It is also shown that random initialization increases the number of roaming particles, and that this has a negative impact on convergence time. It is also shown that enforcing a boundary constraint on personal best positions does not help much to address this problem. The main objective of the article is to show that the best approach is to initialize particles to zero, or random values close to zero, without imposing a personal best bound.