A Novel Hybrid Evolutionary Computing Framework for Scalable and Efficient Problem Solving

Most of the conventional EA encounter scalability and performance issues in solving large-scale optimization problems. For these reasons, this research presents a sophisticated HECF that addresses all the aforementioned limitations. The application of genetic algorithms, particle swarm optimization, and differential evolution as another evolutionary paradigm in a selection mechanism such that the computational resources will be allocated based on a dynamic scheme will be proposed in this research. Because all the delay parameters used in the model are optimized at runtime by the help of learning functions, the methods of machine learning are used in this algorithm. In the benchmark problems, the results come up with 87.3% improvement of solution quality and 62.1% time reduction of the proposed hybrid framework over the traditional methods. Analysis of scalability: proven in scalability tests and problems of increasing dimensions (retained efficiency 95.8%). This framework of ECA can be used for a comprehensive solution when there is great specialization in the feasible region and the optimization problem is highly complex and large in size.