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

Yong Deng1,2, Yazhou Zhang3,4, Xianming Shi5,6

  • 1School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, Guangdong, China.

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|August 25, 2025
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Summary
This summary is machine-generated.

Human-swarm interaction (HSI) strategies enhance bio-inspired swarm intelligence (SI) algorithms like the monkey algorithm (MA). This approach improves optimization accuracy and efficiency in complex problems.

Keywords:
Artificial intelligenceHuman–swarm interactionMonkey algorithmMultidimensional optimizationSwarm intelligence

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional swarm intelligence (SI) algorithms, such as the monkey algorithm (MA), face limitations like premature convergence and computational inefficiency in complex search spaces.
  • Enhancing SI algorithms with human intelligence is a promising avenue for improving their performance and adaptability.

Purpose of the Study:

  • To introduce and evaluate human-swarm interaction (HSI) strategies for augmenting bio-inspired swarm intelligence (SI) algorithms.
  • To address the limitations of the traditional monkey algorithm (MA) by integrating human intelligence for enhanced optimization.
  • To assess the effectiveness of HSI strategies in improving accuracy, stability, and efficiency in complex optimization tasks.

Main Methods:

  • Proposed three HSI integration strategies: intermittent, persistent, and parameter-setting interactions.
  • Validated the HSI-enhanced MA (HSI-MA) using seven benchmark functions (one unimodal, six multimodal) across seven dimensions.
  • Evaluated HSI-MA performance against the original MA and four baseline SI algorithms.
  • Assessed HSI-MA on five engineering design problems, comparing it with 36 state-of-the-art optimizers.

Main Results:

  • HSI-MA demonstrated statistically significant (p < 0.05) superior accuracy and stability compared to MA and baseline SI algorithms.
  • Achieved 85% dominance in benchmark test cases and reduced iterations by an order of magnitude.
  • Outperformed 36 state-of-the-art optimizers in 70% of engineering design problem scenarios.
  • Confirmed enhanced precision and efficiency in practical applications.

Conclusions:

  • Human-swarm interaction (HSI) strategies effectively enhance bio-inspired swarm intelligence (SI) algorithms, particularly the monkey algorithm (MA).
  • The proposed HSI framework improves optimization accuracy, stability, and computational efficiency in complex and multidimensional problems.
  • HSI offers a novel approach to systematically integrate human intelligence into SI, preserving theoretical foundations while boosting adaptability and performance.