This study addresses the challenge of optimizing the design of reinforced concrete columns, a complex engineering problem involving nonlinear analysis. Traditional trial-and-error methods are often used to determine the optimal sizes of structural members, considering factors like load resistance, cost efficiency, and aesthetics. This study aims to minimize the design cost of reinforced concrete columns by employing three novel human-inspired optimization algorithms: the Gaining-sharing Knowledge-based Algorithm (GSKA), Human Conception Optimizer (HCO), and War Strategy Optimization (WSO). These algorithms are applied for the first time to this problem, which was previously solved using three different swarm-based algorithms, such as the Coati Optimization Algorithm (COA), Fox Optimizer (FOX), and Pelican Optimization Algorithm (POA), focusing on discrete design variables such as the type of steel rebar distribution, concrete strength, column height and width, and rebar characteristics (number and diameter). The study also ensures that the design meets the Turkish Building Earthquake Code 2018 (TBEC 2018) specifications, which serve as design constraints. The performances of those novel human-inspired algorithms and formerly reported swarm-based algorithms are thoroughly compared. It is finally deducted that the novel human-inspired GSKA approach presents a promising alternative to traditional optimization methods for reinforced concrete column design, offering significant improvements in cost and design efficiency.