Bare-bones particle swarm optimization with disruption operator

Bare-bones particle swarm optimization (BPSO) is attractive since it is easy to implement and parameter-free. However, it suffers from premature convergence because of quickly losing diversity. To enhance population diversity and speed up convergence rate of BPSO, this paper proposes a novel disruption strategy, originating from astrophysics, to shift the abilities between exploration and exploitation during the search process. We research the distribution and diversity on the proposed disruption operator, and illustrate the position relationship between the original and disrupted position. The proposed Disruption BPSO (DBPSO) has also been evaluated on a set of well-known nonlinear benchmark functions and compared with several variants of BPSO and other evolutionary algorithms (such as DE, ABC, ES and BSA). Experimental results and statistic analysis confirm promising performance of DBPSO with least computation cost in solving major nonlinear functions.