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Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function

Jie-sheng Wang1, Shu-xia Li2, Jiang-di Song2

  • 1School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning, Anshan 114044, China.

Computational Intelligence and Neuroscience
|September 15, 2015
PubMed
Summary
This summary is machine-generated.

A new repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) cuckoo search (CS) algorithm improves function optimization. This enhanced CS algorithm demonstrates superior convergence velocity and optimization accuracy compared to existing methods.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Swarm intelligence

Background:

  • The cuckoo search (CS) algorithm is a popular metaheuristic for function optimization.
  • Existing CS algorithms can face challenges with convergence velocity and optimization accuracy.
  • Improvements are needed to enhance the performance of CS for complex optimization tasks.

Purpose of the Study:

  • To propose an improved cuckoo search algorithm, termed RC-SSCS, for enhanced function optimization.
  • To introduce a novel disturbance operation with a disturbance factor for more thorough searching.
  • To investigate the optimal number of disturbance times for improved algorithm performance.

Main Methods:

  • Development of the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) algorithm.
  • Integration of a disturbance operation with a disturbance factor near nest locations.
  • Comparative simulation experiments using six standard test functions.
  • Benchmarking against particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms.

Main Results:

  • The proposed RC-SSCS algorithm exhibits significantly improved convergence velocity.
  • The RC-SSCS algorithm demonstrates enhanced optimization accuracy.
  • Comparative results indicate superior performance over PSO and ABC algorithms on tested functions.

Conclusions:

  • The RC-SSCS algorithm effectively addresses the limitations of the standard CS algorithm.
  • The novel disturbance strategy contributes to better exploration and exploitation.
  • RC-SSCS offers a promising alternative for complex function optimization problems.