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A Deep Backtracking Bare-Bones Particle Swarm Optimisation Algorithm for High-Dimensional Nonlinear Functions
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DOI：10.1049/cit2.70028.png)
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Abstract
En 中文
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.
Keywords:
artificial intelligence
computational intelligence
optimisation
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651
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