<p>Differential Evolution (DE) is a population-based meta-heuristic algorithm widely recognized for its simplicity, efficiency, and strong global optimization capabilities. However, DE often encounters challenges such as premature convergence and entrapment in local optima. To address these limitations, this paper proposes an enhanced DE variant incorporating an optimal individual guidance strategy. This strategy improves population adaptability and diversity by continuously guiding the best-performing individuals throughout the evolutionary process. The primary innovation involves introducing additional mutation and guidance mechanisms for the optimal individuals during later iterations, which significantly reduces the likelihood of the algorithm converging prematurely to suboptimal solutions. Unlike complexity-driven DE variants, proposed DE variant achieves competitive results with minimal computational overhead and zero additional parameters. We rigorously evaluate the proposed approach using benchmark functions from the IEEE Congress on Evolutionary Computation (CEC), including CEC2017, CEC2019, and CEC2022 problems. Experimental results demonstrate superior performance across multiple benchmarks, effectively mitigating issues of premature convergence and local optima. Compared with state-of-the-art DE variants, the proposed method exhibits substantial improvements in both convergence speed and global optimization capability.</p>