Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

Authors

  • Noorbhasha Junnu Babu Madanapalle Institute of Technology and Science, Madanapalle
  • Vidya Kamma Neil Gogte Institute of Technology, Rangareddy, Hyderabad, Telangana
  • R. Logesh Babu Department of Computer Science and Business Systems KPR Institute of Engineering and Technology Avinashi Road, Arasur, Coimbatore, 641407, India
  • J. William Andrews Computer science Engineering, Panimalar Engineering college, Chennai.
  • Tatiraju.V.Rajani Kanth Senior Manager,TVR Consulting Services Private Limited Gajularamaram, Medchal Malkangiri district, HYDERABAD- 500055,Telegana,INDIA
  • J. R. Vasanthi Department of CSE, St.Joseph's Institute of Technology

DOI:

https://doi.org/10.22399/ijcesen.834

Keywords:

Quantum-Inspired Evolutionary, Deep learning, Computational Intelligence, Global Search Ability, Particle Swarm Optimization, Solution Accuracy

Abstract

The rapid advancement of computational intelligence (CI) techniques has enabled the development of highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative computational intelligence frameworks that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, and Deep Learning-based optimization models. These frameworks aim to address optimization challenges by improving convergence rates, solution accuracy, and computational efficiency. In the context of healthcare, a Deep Learning-based optimization framework was successfully used to predict the optimal treatment plans for cancer patients, achieving a 92% accuracy rate in classification tasks. The proposed frameworks demonstrate the potential for addressing a broad spectrum of complex problems, from resource allocation in smart grids to dynamic scheduling in manufacturing systems. The integration of cutting-edge CI methods offers a promising future for optimizing performance and solving real-world problems in a wide range of industries.

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Published

2025-01-09

How to Cite

Noorbhasha Junnu Babu, Vidya Kamma, R. Logesh Babu, J. William Andrews, Tatiraju.V.Rajani Kanth, & J. R. Vasanthi. (2025). Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.834

Issue

Vol. 11 No. 1 (2025): IJCESEN

Section

Research Article

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