With the advent of internet technologies, accessing information has become remarkably facile, while concurrently precipitating copyright conundrums. This predicament can be ameliorated by embedding copyright information within digital images, a methodology termed digital image watermarking. Artificial intelligence optimization algorithms are extensively employed in myriad problem-solving scenarios, yielding efficacious outcomes. This study proposes a visible digital image watermarking method utilizing the Single Candidate Optimizer (SCO). Contrary to many prevalent metaheuristic optimization algorithms, SCO, introduced in 2024, is not population-based. The fitness function of SCO is designed to maximize the resemblance between the watermarked image and both the host and watermark images. Experiments were conducted on images commonly utilized in image processing, and the results were evaluated using eight quality metrics. Additionally, the obtained numerical results were juxtaposed with those from well-known and widely-used genetic algorithms, differential evolution algorithms, and artificial bee colony optimization algorithms. The findings demonstrate that SCO outperforms the others in visible digital image watermarking. Furthermore, due to its non-population-based nature, SCO is significantly faster compared to its counterparts.