Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

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.