A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of selfrectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.