  We propose a new class of interacting Markov chain Monte Carlo (MCMC)
algorithms designed for increasing the efficiency of a modified multiple-try
Metropolis (MTM) algorithm. The extension with respect to the existing MCMC
literature is twofold. The sampler proposed extends the basic MTM algorithm by
allowing different proposal distributions in the multiple-try generation step.
We exploit the structure of the MTM algorithm with different proposal
distributions to naturally introduce an interacting MTM mechanism (IMTM) that
expands the class of population Monte Carlo methods. We show the validity of
the algorithm and discuss the choice of the selection weights and of the
different proposals. We provide numerical studies which show that the new
algorithm can perform better than the basic MTM algorithm and that the
interaction mechanism allows the IMTM to efficiently explore the state space.
