Swarm intelligence (SI) algorithms that are based on how animals behave in nature have gotten a lot of interest for addressing hard optimization problems. The Whale Optimization Algorithm (WOA) is focused on how humpback whales catch their prey with bubble nets, Harris Hawks Optimization Algorithm (HHOA) is contingent on how Harris hawks hunt together, and Sine Cosine Algorithm (SCA) implements sine and cosine computations to enable individuals to move closer or farther from the most superior solution it has found so far. This study compares these three well-known nature-inspired metaheuristic algorithms. It tests three algorithms on a set of standard benchmark functions on MATLAB software, including unimodal and multimodal functions, to see how well they enable exploration and exploitation. Multiple independent runs are used to look at performance parameters like convergence speed, solution accuracy, and resilience. The results of the simulation show that HHOA can explore the whole space and converge quickly on benchmark functions that are actually unimodal and multimodal. On the other hand, WOA and SCA always do a tremendous job of avoiding local optima and keeping solutions different. The study’s results confirm HHOA’s excellent performance and stability, which strengthens its promise as a good solution for hard and changing optimization problems. The results also provide useful real-world proof of the relative strengths of each algorithm, giving researchers and engineer’s useful information to help them choose the best optimization method for a given problem. This work not only helps people understand these algorithms better, but it also sets a benchmark for future comparisons of metaheuristics.