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Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction

Author

Listed:
  • Hannah H. Inbarani

    (Department of Computer Science, Periyar University, Salem, India)

  • Ahmad Taher Azar

    (Prince Sultan University, Riyadh, Saudi Arabia & Benha University, Benha, Egypt)

  • Ahmad Taher Azar

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)

  • Bagyamathi Mathiyazhagan

    (Gonzaga College of Arts and Science for Women, Krishnagiri, India)

Abstract

In this paper, a new approach for hybridizing Rough Set Quick Reduct and Relative Reduct approaches with Black Hole optimization algorithm is proposed. This algorithm is inspired of black holes. A black hole is a region of spacetime where the gravitational field is so strong that nothing— not even light— that enters this region can ever escape from it. Every black hole has a mass and charge. In this Algorithm, each solution of problem is considered as a black hole and gravity force is used for global search and the electrical force is used for local search. The proposed algorithm is compared with leading algorithms such as, Rough Set Quick Reduct, Rough Set Relative Reduct, Rough Set particle swarm optimization based Quick Reduct, Rough Set based PSO Relative Reduct, Rough Set Harmony Search based Quick Reduct, and Rough Set Harmony Search based Relative Reduct.

Suggested Citation

  • Hannah H. Inbarani & Ahmad Taher Azar & Ahmad Taher Azar & Bagyamathi Mathiyazhagan, 2022. "Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 14(1), pages 1-44, January.
    • Handle: RePEc:igg:jskd00:v:14:y:2022:i:1:p:1-44
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