Clustering Based Genetic Algorithm in Addressing Combinatorial Optimization Problems

The combination of clustering methods with Genetic Algorithms (GAs) for addressing the Knapsack problem results in considerable increases in optimization efficiency and solution quality. This study investigates many hybrid models, including the usage of DBSCAN, K-means, Affinity Propagation, and Mean Shift clustering algorithms mixed with GAs. Using clustering approaches, the solution space is efficiently partitioned, which improves the evolutionary search process. The Genetic Algorithm with Formal Concept Analysis (FCA)-based Clustering is a revolutionary technique in this work, since it refines the clustering phase using FCA principles. This approach not only finds noteworthy patterns in the data, but it also enhances the whole optimization process by providing a systematic framework for conceptual clustering of solutions. The suggested hybrid algorithms were tested in a number of experiments, proving their capacity to balance exploration and exploitation in the search space. The results show that clustering-based GAs outperform standard GAs in terms of convergence rates and solution quality. Specifically, the FCA-based clustering strategy performed well in big datasets and challenging optimization problems by preserving genetic diversity and preventing premature convergence. The combination of GAs and sophisticated clustering techniques yields a stable and scalable solution to the Knapsack problem and other combinatorial optimization problems.