TPDE: A tri-population differential evolution based on zonal-constraint stepped division mechanism and multiple adaptive guided mutation strategies

Differential evolution (DE) has been recognized as one of the most effective algorithms for solving numerical optimization problems. In this paper, we propose a tri-population differ-ential evolution (TPDE) to further enhance the search capability of DE. More specifically, the parent population is partitioned into three sub-populations with different emphasises at each iteration based on a newly proposed zonal-constraint stepped division (ZSD) mech-anism, which determines the size of each sub-population according to not only individual's fitness value but also the evolutionary process. To make the best of information provided by elite individuals and play their leading role on other individuals, three elite-guided mutation strategies are presented for each sub-population. Moreover, three sets of adap-tive control parameters including the scale factor F and crossover rate CR are configured for three mutations according to Gaussian distribution model, Cauchy distribution model and a triangular wave function respectively. The design of mutation strategies and control parameters for each sub-population is based on the principle of balancing the global explo-ration and local exploitation capabilities. To evaluate the performance of TPDE, compara-tive experiments are conducted based on 59 benchmark functions from CEC2014 and CEC2017 test suites. The results indicate that the proposed TPDE is significantly better than, or at least comparable to the recent nine state-of-the-art DE variants. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces a improved tri-population differential evolution (TPDE) algorithm to enhance the search capability of DE through zonal-constraint stepped division mechanism and elite-guided mutation strategies. Experimental results demonstrate that TPDE outperforms or at least is comparable to other nine recent state-of-the-art DE variants in solving numerical optimization problems.