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Cuckoo search algorithm based on cloud model and its application.

Yan Xiong1, Ziming Zou1, Jiatang Cheng2

  • 1College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China.

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|June 21, 2023
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This summary is machine-generated.

A novel cuckoo search algorithm utilizes a cloud model to dynamically adjust its step size factor, improving numerical optimization performance. This adaptive approach enhances efficiency in complex problem-solving.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Numerical Analysis

Background:

  • The Cuckoo Search Algorithm (CSA) is a popular metaheuristic for numerical optimization.
  • A key parameter, the step size factor, significantly impacts CSA's performance and is often sensitive to tuning.
  • Existing CSA methods struggle with optimal parameter selection for diverse problems.

Purpose of the Study:

  • To develop an enhanced Cuckoo Search Algorithm (CSA) with adaptive step size factor control.
  • To leverage the cloud model's ability to handle fuzziness and randomness for parameter optimization.
  • To improve the efficiency and robustness of CSA in numerical optimization tasks.

Main Methods:

  • A novel Cuckoo Search Algorithm (CSA) incorporating a cloud model was developed.
  • The cloud model was used to dynamically determine the step size factor based on membership degree and an exponential function.
  • The algorithm's performance was evaluated using 25 benchmark functions and two chaotic time series prediction problems.

Main Results:

  • The proposed cloud-model-based CSA demonstrated superior performance compared to standard CSA and other non-CSA algorithms.
  • Adaptive step size adjustment led to more competitive results in benchmark function optimization.
  • The algorithm showed effectiveness in complex tasks like chaotic time series prediction.

Conclusions:

  • The integration of the cloud model provides an effective mechanism for adaptive parameter control in CSA.
  • The developed algorithm offers a more robust and efficient solution for numerical optimization problems.
  • This approach advances the application of intelligent optimization techniques in computational science.