Refining swarm behaviors with human-swarm interaction strategies: An improved monkey algorithm for multidimensional optimization problems.

ABSTRACT: This study introduces human-swarm interaction (HSI) strategies to enhance bio-inspired swarm intelligence (SI) algorithms, addressing inherent limitations of the traditional monkey algorithm (MA) such as premature convergence and computational inefficiency in complex search spaces. We propose three HSI integration strategies involving intermittent, persistent, and parameter-setting interactions within the HSI to augment emergent behaviors and refine the MA's intrinsic optimization mechanisms. Validation through seven benchmark functions (one unimodal and six multimodal) across seven dimensions demonstrates the HSI-MA's ability to resolve complex, multidimensional optimization problems with statistically significant (p < 0.05) superior accuracy and stability compared to the original MA and

SUBMITTER: Deng Y 

PROVIDER: S-EPMC12378190 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature