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Optimization of complex engineering problems using modified sine cosine algorithm.

Chao Shang1, Ting-Ting Zhou2, Shuai Liu3

  • 1Pujiang Institute, Nanjing Tech University, Nanjing, 211134, China. shangchao1208@163.com.

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Summary
This summary is machine-generated.

A modified Sine Cosine Algorithm (MSCA) enhances optimization speed and diversity using a new position update and Levy walk mutation. This improved algorithm shows strong performance on benchmark and engineering problems.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • The Sine Cosine Algorithm (SCA) is a popular metaheuristic, but can suffer from slow convergence and premature convergence.
  • Enhancing population diversity is crucial for escaping local optima in complex optimization tasks.

Purpose of the Study:

  • To develop a modified Sine Cosine Algorithm (MSCA) with improved convergence speed and population diversity.
  • To evaluate the performance of MSCA on standard benchmark functions and complex engineering problems.

Main Methods:

  • Redefining the position update mechanism of the original SCA.
  • Incorporating a Levy random walk mutation strategy to enhance population diversity.
  • Benchmarking MSCA against established algorithms on 24 classical problems and IEEE CEC2017 test suites.

Main Results:

  • MSCA demonstrated superior convergence speed compared to the original SCA and other popular optimization methods.
  • The Levy walk mutation effectively improved population diversity, preventing premature convergence.
  • MSCA achieved competitive or superior results on various benchmark functions and engineering design problems.

Conclusions:

  • The proposed MSCA offers significant improvements in convergence and robustness over the standard SCA.
  • MSCA exhibits strong potential for solving complex real-world engineering optimization challenges.