Description: 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.
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Frankensteins_PSO_A_Composite_Particle_Swarm_Optimization_Algorithm-Xkr.pdf
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