An Adaptive Strategy based Large Scale Multiobjective Swarm Optimization through Cosine Similarity

Large-scale multi-objective optimization problems (LSMOPs) present significant challenges due to the difficulty of maintaining convergence and diversity simultaneously in highdimensional decision spaces. Existing strategies, such as problem transformation and learning-based variation operators, often struggle with scalability, which leads to degraded performance as the number of decision variables increases. This paper proposes a novel algorithm, Large-Scale Multi-objective Swarm Optimization based on Cosine Similarity (LSMSO-CS), which integrates adaptive guidance, hybrid diversity preservation, and intelligent learning mechanisms. LSMSO-CS introduces three key innovations, i.e, adaptive K-means clustering to transform nondominated solutions into directional reference vectors, a hybrid crowding metric that combines intra-cluster diversity and proximity to cluster centers for fine-grained selection, and a strategybased learning PSO framework enhanced with non-dominated sorting and cosine similarity-based truncation for environmental selection. Extensive experiments on LSMOPs benchmark suite with decision variables ranging from 1,000 to 8,000 and three objectives show that LSMSO-CS significantly outperforms six state-of-the-art LSMOEAs. The proposed approach consistently achieves superior convergence and diversity, offering a scalable and robust solution framework for complex real-world large-scale multi-objective problems.