This paper verifies the feasibility of implementing swarm intelligence algorithms based on memristors. Taking the Firefly Algorithm (FA) as a representative swarm intelligence method, we binarize it and integrate it with an orthogonal crossing strategy to enhance its global search capability, thus proposing an orthogonal crossingenhanced memristive binary firefly algorithm (MEM-OC-BFA). This algorithm is deployed on a memristor crossbar array to support image threshold segmentation and feature selection tasks. Experimental results show that the proposed MEM-OC-BFA significantly improves the computational efficiency of image segmentation: in fundus image segmentation tasks, its running time is 1.3-2.7 times shorter than that of the standard BFA and 2.8-6.4 times shorter than that of the brute-force search method, while ensuring no loss of segmentation accuracy with an error tolerance of 0.1%. In feature selection tasks across multiple public datasets (e.g., Leukemia, Musk, Sonar), MEM-OC-BFA exhibits significantly superior average accuracy and stability compared to Particle Swarm Optimization (PSO) and memristive binary firefly algorithm with cosine similarity (MEM-CS-BFA).
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