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In swarm intelligence, Particle Swarm Optimization (PSO) and DifferentialEvolution (DE) have been successfully applied in many optimization tasks, and alarge number of variants, where novel algorithm operators or components areimplemented, has been introduced to boost the empirical performance. In thispaper, we first propose to combine the variants of PSO or DE by modularizingeach algorithm and incorporating the variants thereof as different options ofthe corresponding modules. Then, considering the similarity between the innerworkings of PSO and DE, we hybridize the algorithms by creating two populationswith variation operators of PSO and DE respectively, and selecting individualsfrom those two populations. The resulting novel hybridization, called PSODE,encompasses most up-to-date variants from both sides, and more importantlygives rise to an enormous number of unseen swarm algorithms via differentinstantiations of the modules therein.  In detail, we consider 16 different variation operators originating fromexisting PSO- and DE algorithms, which, combined with 4 different selectionoperators, allow the hybridization framework to generate 800 novel algorithms.The resulting set of hybrid algorithms, along with the combined 30 PSO- and DEalgorithms that can be generated with the considered operators, is tested onthe 24 problems from the well-known COCO/BBOB benchmark suite, across multiplefunction groups and dimensionalities.

A Modular Hybridization of Particle Swarm Optimization and Differential Evolution