LCO-LSHADE-GSRL: An enhanced differential evolution algorithm with chaotic orthogonal initialization and GAN-driven specular reflection learning for engineering optimization

Engineering optimization problems are often nonlinear, high-dimensional, and constrained, making them challenging for conventional optimization techniques. Although L-SHADE, an adaptive differential evolution (DE) algorithm with success-history based parameter adaptation, has demonstrated competitive performance, it still suffer from limited population diversity and weak local exploitation, leading to an imbalance between exploration and exploitation in complex optimization. To address these limitations, this paper proposes LCO-LSHADE-GSRL, a novel DE variant that enhances both global exploration and local exploitation capabilities. The proposed algorithm integrates three key components: (1) a Logistic Chaos Orthogonal Initialization mechanism that improves initial population diversity and ensures uniform coverage of the search space. (2) a GAN-driven Specular Reflection Learning (SRL) mechanism that effectively escapes from local optima. (3) a design that adapts effectively to constrained optimization scenarios. Comprehensive experiments conducted on the CEC 2019 and 2022 benchmark suites demonstrate that LCO-LSHADE-GSRL exhibits superior convergence performance, solution accuracy, and robustness compared to L-SHADE, LSHADE-cnEpSin, and WOA, GJO, PO, PIMO, and CDO. Furthermore, in three real-world engineering problems–speed reducer, step-cone pulley, and hydrostatic thrust bearing, which reduces system weight and power loss while satisfying all design constraints. These results demonstrate its potential for solving complex engineering optimization tasks with high reliability and efficiency.