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Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer

Gang Hu1, Jingyu Zhong2, Xupeng Wang3

  • 1Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China; School of Computer Science and Engineering,, Xi'an University of Technology, Xi'an, 710048, PR China.

Computers in Biology and Medicine
|November 6, 2022
PubMed
Summary

The new COBHCOOT algorithm enhances the COOT algorithm by integrating chaos, opposition-based learning, and hunting strategies. This improved metaheuristic algorithm demonstrates superior performance in solving complex optimization problems and medical feature selection tasks.

Keywords:
BenchmarkCOOT optimization algorithmCervical cancer behavior riskChaotic mapEngineering optimizationHunting strategyOpposition-based learning

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Real-world optimization problems necessitate advanced metaheuristic algorithms for effective solution finding.
  • The COOT algorithm, while useful, suffers from common swarm intelligence issues like low diversity and slow convergence.
  • Addressing these limitations is crucial for developing more robust optimization techniques.

Purpose of the Study:

  • To introduce an improved population-initialized COOT algorithm, named COBHCOOT.
  • To enhance global convergence speed, exploration efficiency, and solution quality.
  • To validate the effectiveness of COBHCOOT on benchmark functions and real-world problems.

Main Methods:

  • Integration of a chaos map, opposition-based learning strategy, and hunting strategy into the COOT algorithm.
  • Comparative analysis against the original COOT algorithm and other natural heuristic algorithms on CEC2017 and CEC2019 benchmark suites.
  • Application to engineering optimization problems, truss structure optimization, and medical feature selection datasets.

Main Results:

  • COBHCOOT demonstrated superior performance on a significant portion of CEC2017 and CEC2019 benchmark functions across various dimensions.
  • The algorithm proved effective in solving engineering and truss structure optimization problems.
  • In medical feature selection, COBHCOOT achieved higher accuracy with fewer features, notably on the cervical cancer dataset.

Conclusions:

  • COBHCOOT effectively overcomes the limitations of the original COOT algorithm.
  • The proposed algorithm shows significant potential for both complex optimization tasks and practical applications like medical feature selection.
  • COBHCOOT offers a promising advancement in metaheuristic optimization with improved efficiency and solution quality.