A hybrid multiverse giza pyramid construction metaheuristic for robust engineering optimization

Abstract Achieving an effective balance between global exploration and local exploitation remains a fundamental challenge in metaheuristic optimization, often leading to premature convergence or slow convergence on complex engineering problems. To address this issue, this paper proposes a novel hybrid optimization algorithm, termed Multiverse–Giza Pyramid Construction Optimization (MV-GPCO), which integrates the complementary search mechanisms of the Multiverse Optimization (MVO) and Giza Pyramid Construction (GPC) algorithms within a unified optimization framework. The proposed method combines the stochastic exploration capability of MVO, inspired by white holes, black holes, and wormholes, with the deterministic local refinement strategy of GPC to improve convergence accuracy. This hybrid search mechanism enables efficient optimization in high dimensional and multimodal search spaces while maintaining an improved exploration–exploitation balance. The proposed algorithm is evaluated on CEC2022 benchmark test suite and constrained engineering design problems, and compared with 14 state of the art metaheuristic algorithms. Experimental results demonstrate superior performance, achieving faster convergence, higher solution accuracy, and improved robustness. Statistical analyses further confirm the stability and consistency of the proposed approach across optimization problems with diverse structural characteristics. These results demonstrate that MV-GPCO provides an efficient and reliable optimization framework for challenging engineering optimization applications.