An evolutionary-based optimizer, Differential Evolution (DE) is renowned for its simplicity and strong optimization stability. Numerous efforts have been made to enhance DE’s performance. Unfortunately, it often encounters challenges such as slow convergence speed and stagnation. It happened due to improper control parameter selection or mismatched exploitation and exploration ability. To address such inconveniences, this study proposed a revised differential evolution (ReDE) algorithm to solve unconstrained real-life problems (URLPs). In ReDE, a novel mutation scheme is employed to maintain search efficiency, a new scaling factor is presented to obtain exploratory and exploitative balancing capability, and an advanced crossover rate is used to uphold population diversity. The effectiveness of ReDE was validated by over ten test problems and later applied to three URLPs. Experimental outcomes compared with swarm-based, population-based, bio-based, evolutionary-based, and hybrid optimizers show that the proposed ReDE offers faster convergence speed (owing to better search proficiency), and solution accuracy (due to alleviating the effects of local optimum).