Abstract
Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially
for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization
problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it.
However, the solution space expands exponentially with the dataset's dimensionality. Simultaneously, the
extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated
and redundant features [H. Adeli and H. S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, AI Mag. 17 (1996) 87–93]. Consequently, existing
MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems
(LSMOFSPs). Di®erent LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely
on a single candidate solution generation strategy (CSGS), which may be less e±cient for diverse LSMOFSPs
[H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures,
J. Struct. Eng. ASCE 123 (1997) 880–888; M. Aldwaik and H. Adeli, Advances in optimization of highrise
building structures, Struct. Multidiscip. Optim. 50 (2014) 899–919; E. G. Gonzalez, J. R. Villar, Q. Tan,
J. Sedano and C. Chira, An e±cient multi-robot path planning solution using a* and coevolutionary
algorithms, Integr. Comput. Aided Eng. 30 (2022) 41–52]. Moreover, selecting an appropriate MOEA and
determining its corresponding parameter values for a speci¯ed LSMOFSP is time-consuming. To address these
challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed,
combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along
with ¯ve modi¯ed e±cient CSGSs, to generate new solutions. Experiments were conducted on ten datasets,
and the results demonstrate that the number of features is e®ectively reduced by MOSaPSO while lowering
the classi¯cation error rate. Furthermore, superior performance is observed in comparison to its counterparts
on both the training and test sets, with advantages becoming increasingly evident as the dimensionality
increases.