[论文]洪立斌等人.A variant of the united multi-operator evolutionary algorithms using sequential quadratic programming and improved SHADE-cnEpSin
A variant of the united multi-operator evolutionary algorithms using sequential quadratic programming and improved SHADE-cnEpSin
L. B. Hong, Y. J. Guo, F. C. Liu and B. Wang
Information Sciences 2023 Vol. 622 Pages 652-681
Accession Number: WOS:000900836600001 DOI: 10.1016/j.ins.2022.11.131
United multi-operator evolutionary algorithms (UMOEAs) combine multi-operator differ-ential evolution (DE), the multi-operator genetic algorithm (MOGA), and the covariance matrix adaption evolution strategy (CMA-ES). UMOEAs-II, an improved version of UMOEAs, uses three differential evolution variants as multi-operator differential evolution (MODE), and CMA-ES. In this study, we further reform UMOEAs-II using an improved SHADE-cnEpSin that employs a novel adaptive strategy of scaling factor F, a crossover rate cri;j updating mechanism which can calculate crossover rate for the ith individual with a particular jth component, an improved rank-based selective pressure based mutation strat-egy, and nonlinear population size reduction along with sequential quadratic programming method. The effectiveness of the improved rank-based selective pressure based mutation strategy, nonlinear population size reduction, and sequential quadratic programming are evident from the individual validations. The novel framework, enhanced the exploration and exploitation abilities, is named UMOEAs-III and is evaluated using the CEC2017 bench-mark functions. The experiments are tested on 10, 30, 50, and 100 dimensions. The exper-imental results demonstrate the outstanding performance of UMOEAs-III in both low and high-dimensional tests compared to the state-of-the-art DE-based variants and hybrid algorithms.(c) 2022 Elsevier Inc. All rights reserved.