During the last decade, many variants of the original

particle swarm optimization (PSO) algorithm have been

proposed. In many cases, the difference between two variants can

be seen as an algorithmic component being present in one variant

but not in the other. In the first part of the paper, we present

the results and insights obtained from a detailed empirical

study of several PSO variants from a component difference

point of view. In the second part of the paper, we propose

a new PSO algorithm that combines a number of algorithmic

components that showed distinct advantages in the experimental

study concerning optimization speed and reliability. We call this

composite algorithm Frankenstein’s PSO in an analogy to the

popular character of Mary Shelley’s novel. Frankenstein’s PSO

performance evaluation shows that by integrating components in

novel ways effective optimizers can be designed.