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A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

Author

Listed:
  • Yu Li

    (Institute of Management Science and Engineering and School of Business, Hena University, China)

  • Yiran Zhao

    (School of Business, Henan University, China)

  • Jingsen Liu

    (Institute of Intelligent Network Systems and Software School, Henan University, China)

Abstract

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

Suggested Citation

  • Yu Li & Yiran Zhao & Jingsen Liu, 2021. "A Levy Flight Sine Cosine Algorithm for Global Optimization Problems," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 12(1), pages 49-66, January.
    • Handle: RePEc:igg:jdst00:v:12:y:2021:i:1:p:49-66
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