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A novel particle swarm optimizer with failure-aware searching framework for neural mass model inversion
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DOI：10.1016/j.swevo.2026.102504.png)
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Abstract
En 中文
In this paper, a novel particle swarm optimizer (PSO) is proposed based on a designed failure-aware searching (FAS) framework. Named FAS-PSO, the proposed algorithm seeks to learn potential valuable information from those non-improved evolutionary experiences rather than directly discarding them. Considering the topological status of population, each particle is equipped with a structural probe to assess local regions, which integrates information from different individuals and enhances the communication among population. Next, in case of a probing location with better fitness, the swarm will be updated accordingly. Otherwise, by measuring the deterioration extent of the probe as compared to the initial particle, an information reinvestigation mechanism is adopted to generate a positional modification for swarm update, including continuous forward search and escape in the opposite direction. Extensive experiments demonstrate the performance of FAS-PSO is superior to that of other state-of-the-art swarm intelligence-based algorithms. Moreover, the proposed FAS-PSO is further extended to a multi-objective version and is successfully applied to accomplish the reliable and robust parameter estimation of a Wendling neural mass model, showing considerable engineering practicality in solving the complicated biomedical model inversion task.
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