This paper proposes SHIODEG, a hybrid metaheuristic that integrates the success-history intelligent optimizer (SHIO) with differential evolution (DE) and a Gaussian transformation (GT) to tackle two persistent challenges in optimization for engineering design: (i) the absence of a universally best optimizer across problem classes (as implied by the No-Free-Lunch perspective) and (ii) the limited ability of purely gradient-based methods to produce substantial improvements in complex, constrained, and often non-smooth real-world problems, motivating hybrid strategies that balance exploration and exploitation. SHIODEG follows a staged search process in which DE generates diverse trial solutions, GT injects normally distributed perturbations to reduce premature convergence and diversity collapse, and SHIO refines promising regions using success-history guidance from the best three leaders. SHIODEG is evaluated on the IEEE CEC2022 benchmark suite (12 functions) using 30 independent runs, a population size of 100, and a budget of 1000D function evaluations. The results show that SHIODEG consistently delivers top-tier performance across the benchmark suite, showing strong competitiveness, low variability, and statistically significant improvements over a wide range of alternative optimizers. It also demonstrates robust effectiveness on multiple constrained engineering design problems, achieving high-quality solutions across diverse real-world constraints.