The multiset sampler has been shown to be an effective algorithm to sample fromcomplex multimodal distributions, but the multiset sampler requires that the parameters in the target distribution can be divided into two parts: the parameters of interest and the nuisance parameters. We propose a new self-multiset sampler (SMSS) which extends the multiset sampler to distributions without nuisance parameters. We also generalize our method to distributions with unbounded or infinite support. Finally, we demonstrate the SMSS and its generalization to several examples and compare it with ordinary MCMC and some popular variants. Numerical results show the advantage of our methods over the other methods in sampling multimodal distributions. This is joint work with Weihong Huang and Yuguo Chen.