A Deep Backtracking Bare‐Bones Particle Swarm Optimisation Algorithm for High‐Dimensional Nonlinear Functions

ABSTRACT The challenge of optimising multimodal functions within high‐dimensional domains constitutes a notable difficulty in evolutionary computation research. Addressing this issue, this study introduces the Deep Backtracking Bare‐Bones Particle Swarm Optimisation (DBPSO) algorithm, an innovative...

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Bibliographic Details
Published in:CAAI Transactions on Intelligence Technology Vol. 10; no. 5; pp. 1501 - 1520
Main Authors: Guo, Jia, Zhou, Guoyuan, Yan, Ke, Di, Yi, Sato, Yuji, He, Zhou, Shi, Binghua
Format: Journal Article
Language:English
Published: Wiley 01.10.2025
Subjects:
artificial intelligence
computational intelligence
optimisation
ISSN:2468-2322, 2468-6557, 2468-2322
Online Access:Get full text
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Summary:ABSTRACT The challenge of optimising multimodal functions within high‐dimensional domains constitutes a notable difficulty in evolutionary computation research. Addressing this issue, this study introduces the Deep Backtracking Bare‐Bones Particle Swarm Optimisation (DBPSO) algorithm, an innovative approach built upon the integration of the Deep Memory Storage Mechanism (DMSM) and the Dynamic Memory Activation Strategy (DMAS). The DMSM enhances the memory retention for the globally optimal particle, promoting interaction between standard particles and their historically optimal counterparts. In parallel, DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository. The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite. A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions: Dimension‐50 and Dimension‐100. In the 50D trials, DBPSO attained an average ranking of 2.03, whereas in the 100D scenarios, it improved to an average ranking of 1.9. Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness, securing four first‐place finishes, three second‐place standings, and three third‐place positions, culminating in an unmatched average ranking of 1.9 across all algorithms. These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex, high‐dimensional optimisation challenges.
Bibliography:Funding
This work was supported by the Artificial Intelligence Innovation Project of Wuhan Science and Technology Bureau, 2023010402040016, the Natural Science Foundation of Hubei Province of China, 2022CFB076, JSPS KAKENHI, JP25K15279, Natural Science Foundation of Hubei Province, 2023AFB003, the National Natural Science Foundation of China, 52201363, and the Education Department Scientific Research Programme Project of Hubei Province of China, Q20222208.
ISSN:2468-2322
2468-6557
2468-2322
DOI:10.1049/cit2.70028