In view of the easy entrapment into local optima and sensitiveness to initial point of conventional clustering methods, this paper proposed an improved heap based optimizer(HBO) clustering method. Firstly, this paper presented an improved HBO, namely DRHBO. It used a random dimensional value replacement and Gaussian disturbance strategy to update the state of the best agent to solve the defects such as low efficiency of HBO. It utilized a sine differential disturbance to update a random agent' state and that breaks through the shackle of the individual's communication only with its direct leader and colleagues, to improve the search ability. It integrated the random dimensional value replacement and differential disturbance strategies to update the states of the agents in the initial stage of HBO to avoid generating inefficient solutions. Secondly, this paper presented a DRHBO clustering method and applied it to cervical cell data to get better effects. Lots of experimental results on cervical cell data sets with diverse types and different sample numbers show that compared with HBO, its variants and other state-of-the-art algorithms, DRHBO can get better performance, stronger stability and higher efficiency. DRHBO clustering is more suitable to cervical cell data.

[2023-06-28] Accepted Paper /doc/2023.05.0153/v1

[2023-12-05] Printed Article /doc/2023.05.0153/v2