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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm.

Hongwei Kang1, Fengfan Bei1, Yong Shen1

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Swarm intelligence algorithms can prematurely converge on complex problems. This study introduces dimension entropy and a diversity control mechanism to maintain population diversity, improving algorithm performance.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Swarm intelligence algorithms excel at optimization due to self-organization and adaptation.
  • Traditional swarm intelligence methods suffer from premature convergence and loss of population diversity in complex, multi-peak problems.

Purpose of the Study:

  • To address the premature convergence issue in swarm intelligence algorithms.
  • To propose a novel approach for maintaining population diversity in complex optimization tasks.

Main Methods:

  • Introduced dimension entropy as a metric for quantifying population diversity.
  • Developed a diversity control mechanism to guide swarm intelligence algorithm updates.
  • Implemented and tested the improved algorithm against traditional methods.

Main Results:

  • The proposed dimension entropy effectively measures population diversity.
  • The diversity control mechanism successfully maintains algorithm diversity in early stages and ensures convergence in later stages.
  • Experimental results demonstrate superior performance of the improved algorithm compared to the original swarm intelligence algorithm.

Conclusions:

  • The integration of dimension entropy and a diversity control mechanism enhances swarm intelligence algorithm performance.
  • This approach effectively mitigates premature convergence in high and complex multi-peak optimization problems.
  • The proposed method offers a robust solution for improving the efficiency and effectiveness of swarm intelligence algorithms.