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A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems.

Zikai Wang1, Xueyu Huang1, Donglin Zhu1

  • 1School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China.

Computational Intelligence and Neuroscience
|March 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved sparrow search algorithm (IHSSA) to overcome local optima in optimization. IHSSA enhances population diversity and solution quality, demonstrating superior performance in engineering problems.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Nature-inspired computing

Background:

  • Swarm intelligence algorithms, inspired by natural biological behavior, are widely applied.
  • The sparrow search algorithm (SSA) shows strong optimization capabilities but can easily fall into local optima.
  • Addressing SSA's limitations is crucial for enhancing its practical application in complex optimization tasks.

Purpose of the Study:

  • To propose an improved sparrow search algorithm (IHSSA) that mitigates the local optimum problem.
  • To enhance the exploration and exploitation balance of the sparrow search algorithm.
  • To validate the effectiveness of IHSSA on benchmark test functions and constrained engineering optimization problems.

Main Methods:

  • The IHSSA integrates infinitely folded iterative chaotic mapping (ICMIC) for population initialization and a hybrid reverse learning strategy for updating discoverers and the global worst solution.
  • A crossover strategy is employed to update scout positions, balancing exploration and exploitation.
  • Experiments were conducted using 14 common test functions, and the Wilcoxon rank sum test was used for statistical validation.

Main Results:

  • The IHSSA demonstrated higher accuracy and better convergence performance compared to nine other algorithms, including WOA, GWO, PSO, TLBO, and SSA variants.
  • Statistical analysis using the Wilcoxon rank sum test confirmed the significant improvement of IHSSA.
  • Application to three constrained engineering optimization problems yielded satisfactory results, confirming IHSSA's effectiveness and feasibility.

Conclusions:

  • The proposed IHSSA effectively addresses the local optimum issue inherent in the standard sparrow search algorithm.
  • IHSSA exhibits superior performance in terms of accuracy and convergence speed for optimization tasks.
  • The algorithm's successful application to engineering problems highlights its practical utility and robustness.