cIGW-SC: an intelligent chaotic hybrid grey wolf and sine cosine optimizer for complex engineering design problems

For intelligent systems designed to solve complex engineering design problems, premature convergence and the imbalance between exploration and exploitation remain key challenges for high-dimensional, multimodal, and composite problems. Existing approaches, including the Grey Wolf Optimizer (GWO) and Sine Cosine Algorithm (SCA), often suffer from insufficient population diversity and weak memory-guided search mechanisms, leading to stagnation in local optima. To overcome these limitations, this paper proposes an intelligent cIGW-SC, a chaotic hybrid swarm-based optimization algorithm that integrates an Improved Grey Wolf Optimizer (IGWO) with the SCA through a piecewise chaotic map combined within the position-updating strategy. The proposed method is comprehensively evaluated on 72 benchmark functions derived from the CEC 2017, CEC 2019, CEC 2020, CEC 2022, and unimodal/multimodal benchmark suites, and compared against state-of-the-art optimization algorithms. Experimental results show that cIGW-SC obtained the best overall performance on the majority of the benchmark functions, corresponding to a success rate of 94%. Category-wise analysis further demonstrates strong robustness, achieving leading performance across all benchmark groups. To validate its practical applicability for engineering applications, cIGW-SC is additionally applied to diverse real-world scenarios, including gear train, compression spring, welded beam, pressure vessel, cantilever beam, transmission line capacitance estimation, three-bar truss optimization, and hydrothermal scheduling problem. In all scenarios, the proposed method achieves superior optimization results compared with existing algorithms. Statistical analyses using Friedman and Wilcoxon tests confirm that the observed improvements are statistically significant. Therefore, the findings demonstrate that cIGW-SC provides an effective balance between exploration and exploitation, offering high accuracy, robustness, and intelligent practical applicability for complex engineering design problems.