  In swarm intelligence, Particle Swarm Optimization (PSO) and Differential
Evolution (DE) have been successfully applied in many optimization tasks, and a
large number of variants, where novel algorithm operators or components are
implemented, has been introduced to boost the empirical performance. In this
paper, we first propose to combine the variants of PSO or DE by modularizing
each algorithm and incorporating the variants thereof as different options of
the corresponding modules. Then, considering the similarity between the inner
workings of PSO and DE, we hybridize the algorithms by creating two populations
with variation operators of PSO and DE respectively, and selecting individuals
from those two populations. The resulting novel hybridization, called PSODE,
encompasses most up-to-date variants from both sides, and more importantly
gives rise to an enormous number of unseen swarm algorithms via different
instantiations of the modules therein.
  In detail, we consider 16 different variation operators originating from
existing PSO- and DE algorithms, which, combined with 4 different selection
operators, allow the hybridization framework to generate 800 novel algorithms.
The resulting set of hybrid algorithms, along with the combined 30 PSO- and DE
algorithms that can be generated with the considered operators, is tested on
the 24 problems from the well-known COCO/BBOB benchmark suite, across multiple
function groups and dimensionalities.
