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.