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Abstract Here we present a dimer swarm optimizer (DSO) that hybridizes a particle swarm optimizer (PSO) (Dresselhuas et al., 2013) and the stochastic surface walking SSW (Shang and Liu, 2013)… Click to show full abstract
Abstract Here we present a dimer swarm optimizer (DSO) that hybridizes a particle swarm optimizer (PSO) (Dresselhuas et al., 2013) and the stochastic surface walking SSW (Shang and Liu, 2013) method. The DSO has additional biasing modes in the dimer rotation step in order to drive an individual towards the global, iteration and neighboring best particles in the swarm. The DSO was augmented with an adaptive Gaussian half-width and Monte Carlo temperature scheme. DSO has been validated on three LJ n clusters of increasing sizes ( n = 31, 38 and 55).
Journal Title: Computational and Theoretical Chemistry
Year Published: 2017
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