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Updated: Jul 7, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization

Junjie Tang1, Lianguo Wang1

  • 1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.

Biomimetics (Basel, Switzerland)
|December 22, 2023
PubMed
Summary

A novel Sine Cosine Algorithm for Elite Individual Collaborative Search improves optimization accuracy and convergence speed. This enhanced algorithm overcomes limitations of traditional methods, demonstrating superior performance in simulations and mechanical design experiments.

Keywords:
co-optimizationelite individualglobal optimizationm-neighborhoodmechanical design optimizationnonlinearityoptimization problemsine cosine algorithmswarm intelligencetanh parametertent chaotic mapping

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Traditional Sine Cosine Algorithm (SCA) suffers from low search accuracy, slow convergence, and premature local optima.
  • Existing intelligent optimization algorithms often struggle to balance global exploration and local exploitation effectively.
  • Need for improved optimization techniques applicable to complex engineering design problems.

Purpose of the Study:

  • To propose an enhanced Sine Cosine Algorithm for Elite Individual Collaborative Search (SCA-EICS) to address SCA limitations.
  • To improve population distribution, global exploration, and local exploitation balance.
  • To enhance convergence accuracy and speed while avoiding local optima.

Main Methods:

  • Population initialization using tent chaotic mapping for improved distribution.
  • Non-linear adjustment of SCA parameters using hyperbolic tangent function.
  • Hybrid search strategy combining SCA, m-neighborhood local search, and global best search, executed alternately.
  • Greedy selection strategy to accelerate convergence.

Main Results:

  • SCA-EICS demonstrated superior optimization performance compared to standard SCA, other improved SCA variants, chaos-based algorithms, and other intelligent optimizers.
  • The proposed algorithm achieved better convergence accuracy and faster convergence speed.
  • Effectiveness validated through simulations and two mechanical optimization design experiments.

Conclusions:

  • The Sine Cosine Algorithm for Elite Individual Collaborative Search effectively overcomes the shortcomings of the standard SCA.
  • The hybrid search strategy and parameter adjustment significantly enhance optimization capabilities.
  • SCA-EICS shows strong feasibility and applicability for real-world engineering optimization tasks.