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Research on multi-strategy improved sparrow search optimization algorithm.

Teng Fei1, Hongjun Wang1, Lanxue Liu1

  • 1Institute of Information Engineering, Tianjin University of Commerce, Tianjin, China.

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Summary
This summary is machine-generated.

This study introduces an improved sparrow search algorithm (ISSA) to overcome limitations in optimization. The enhanced algorithm demonstrates superior convergence, accuracy, and global optimization capabilities for complex problems.

Keywords:
honeypot optimization algorithmperturbation operatorpilot optimizationpopulation dynamic adjustmentsparrow search algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The standard sparrow search algorithm (SSA) suffers from limited search space, slow convergence, and a tendency to get stuck in local optima.
  • These limitations hinder its effectiveness in solving complex optimization problems.
  • Addressing these issues is crucial for advancing swarm intelligence algorithms.

Purpose of the Study:

  • To develop a multi-strategy improved sparrow search algorithm (ISSA) that enhances the SSA's performance.
  • To improve global exploration and the ability to escape local optima.
  • To validate the effectiveness of ISSA through benchmark testing and engineering applications.

Main Methods:

  • Implemented a population dynamic adjustment strategy to regulate discoverer and joiner populations.
  • Integrated the honeypot optimization algorithm's (HBA) update strategy to refine joiner position updates.
  • Applied a perturbation operator and Lévy flight strategy to the discoverer's optimal position for enhanced local search.
  • Evaluated ISSA against SSA and four other swarm intelligence algorithms using 13 benchmark functions.
  • Utilized the Wilcoxon rank sum test for statistical significance analysis.

Main Results:

  • ISSA exhibited significantly improved convergence speed and solution accuracy compared to baseline SSA and other algorithms.
  • The enhanced global exploration and local search capabilities of ISSA were demonstrated.
  • Statistical tests confirmed the significant performance improvement of ISSA.
  • ISSA achieved a notable reduction in bit error rate when applied to pilot optimization in channel estimation.

Conclusions:

  • The multi-strategy improved sparrow search algorithm (ISSA) effectively addresses the limitations of the standard SSA.
  • ISSA offers superior performance in terms of convergence, accuracy, and global optimization.
  • The algorithm shows significant potential for practical engineering applications, particularly in channel estimation.