Transitional Particle Swarm Optimization

A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs

Particle swarm optimization using dimension selection methods

a b s t r a c t Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the PSO, and then gives a PSO variant without randomness to show that traditional PSO cannot work without randomness. Based on our analysis of the randomness, another way of using randomness is proposed in PSO with random dimension selection (PSORDS) algorithm, which utilizes random dimension selection instead of stochastic coefficients. Finally, deterministic methods to do the dimension selection are proposed, and the resultant PSO with distance based dimension selection (PSODDS) algorithm is greatly superior to the traditional PSO and PSO with heuristic dimension selection (PSOHDS) algorithm is comparable to traditional PSO algorithm. In addition, using our dimension selection method to a newly proposed modified particle swarm optimization (MPSO) algorithm also gets improved results. The experiment results demonstrate that our analysis about the randomness is correct and the usage of deterministic dimension selection method is very helpful