Optimization algorithms play a critical role in addressing complex problems across diverse fields such as engineering, economics, and data science. This study conducts an in-depth comparative analysis of the Continuous Mycorrhiza Optimization Algorithm (CMOA) and the Discrete Mycorrhiza Optimization Algorithm (DMOA) by applying them to 36 mathematical benchmark functions. These functions span various types of optimization challenges, including unimodal, multimodal, separable, and non-separable problems, providing a comprehensive assessment of the algorithms’ performance. The evaluation criteria include convergence speed, solution accuracy, robustness, and computational efficiency. CMOA, designed for continuous problem spaces, is analyzed for its ability to achieve smooth convergence towards optimal solutions, particularly in problems with continuous search spaces. Its adaptability to parameter variations and the dynamics of the continuous domain is explored in detail. Conversely, DMOA, tailored for discrete optimization tasks, is examined for its effectiveness in managing solution diversity and handling discrete variables with a more precise exploitation of the solution space. The study highlights the distinctive advantages of each algorithm, such as CMOA’s superior performance in maintaining stability across complex continuous landscapes and DMOA’s flexibility in addressing combinatorial and discrete optimization challenges. Results from the benchmark tests reveal that CMOA generally excels in scenarios with a smooth fitness landscape, showing faster convergence rates and higher precision in continuous domains. On the other hand, DMOA demonstrates greater robustness in discrete scenarios, preventing premature convergence and ensuring diverse solutions. This analysis provides valuable insights into the selection of optimization strategies depending on the problem characteristics, offering a practical guide for researchers in selecting the most suitable algorithm for specific applications. The findings also suggest potential directions for future research, such as hybrid approaches that combine both CMOA and DMOA to tackle increasingly complex optimization problems .