Solving the Task of Local Optima Traps in Data Mining Applications through Intelligent Mult-Agents Swarm and Orthopair Fuzzy Sets

Local optima traps pose a significant challenge in optimizing complex problems, particularly in data mining applications, where traditional algorithms may get stuck in suboptimal solutions. This study addresses this issue by combining the power of intelligent multi-agent swarm algorithms and orthopair fuzzy sets to enhance optimization processes. We propose a novel approach that leverages the collective intelligence of a multi-agent swarm system, enabling effective exploration and exploitation of solution spaces. Additionally, orthopair fuzzy sets are introduced to model and represent uncertainties inherent in data mining tasks, providing a more robust optimization framework. Our work contributes to the advancement of optimization techniques in data mining by offering a synergistic solution to local optima traps. The integration of intelligent multi-agent swarms and orthopair fuzzy sets enhances the algorithm’s adaptability and resilience, leading to improved convergence and better solutions. Experimental results demonstrate the efficacy of our proposed approach in overcoming local optima traps, showcasing superior performance compared to traditional algorithms. The hybrid system exhibits increased convergence rates and consistently discovers more accurate and diverse solutions across various data mining scenarios.