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A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and

Osman Altay1, Elif Varol Altay1

  • 1Software Engineering, Manisa Celal Bayar University, Manisa, Turkey.

Peerj. Computer Science
|September 14, 2023
PubMed
Summary

This study introduces the Chaotic Transient Search Algorithm (CTSO), enhancing metaheuristic optimization by integrating chaotic maps. CTSO improves convergence speed and accuracy, outperforming standard algorithms in various optimization and feature selection tasks.

Keywords:
Benchmark functionsChaotic mapsChaotic transient search optimization algorithmFeature selectionReal-world engineering problems

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Metaheuristic optimization algorithms are crucial for complex problems.
  • The Transient Search Algorithm (TSO) shows promise but suffers from local optima and slow convergence.
  • Hybridization and integration of methods, like chaotic maps, can enhance metaheuristic performance.

Purpose of the Study:

  • To improve the performance of the Transient Search Algorithm (TSO).
  • To accelerate global convergence and enhance accuracy by integrating chaotic maps into TSO.
  • To evaluate the effectiveness of the proposed Chaotic Transient Search Algorithm (CTSO) in global optimization, real-world engineering problems, and feature selection.

Main Methods:

  • Integration of 10 different chaotic maps into the TSO algorithm to generate chaotic values instead of random values.
  • Evaluation of the Chaotic Transient Search Algorithm (CTSO) on IEEE CEC'17 benchmarking functions for global optimization.
  • Testing CTSO on real-world engineering design problems (speed reducer, tension compression spring, welded beam, pressure vessel, three-bar truss).
  • Assessing CTSO's performance as a feature selection method on 10 UCI standard datasets.

Main Results:

  • The Chaotic Transient Search Algorithm (CTSO) demonstrated superior performance compared to the standard TSO and other competitive metaheuristic methods.
  • Gaussian and Sinusoidal chaotic maps showed significant improvements in benchmark function optimization.
  • The Sinusoidal map was particularly effective for real-world engineering problems.
  • CTSO proved effective as a feature selection method across various datasets.

Conclusions:

  • The integration of chaotic maps significantly enhances the performance of the Transient Search Algorithm (TSO).
  • CTSO offers improved convergence speed, accuracy, and the ability to escape local optima.
  • The proposed Chaotic Transient Search Algorithm (CTSO) is a more effective optimization and feature selection approach than the standard TSO.