This paper presents a new approach to the circulatory system-based optimization algorithm called NCSBO. The proposed method incorporates an adaptive parameter-tuning strategy and a refined mutation approach to improve the performance of optimizing the shape and size of truss structures. The adaptive parameter tuning dynamically adjusts the number of weaker solutions according to the diversity of the population, enabling the algorithm to intelligently balance exploration and exploitation. The mutation strategy selectively alters only one variable of the best solution, preserving its overall structure while conducting a detailed local search to exploit promising regions more efficiently. The validation of the proposed algorithm involves four different truss examples. The design constraints include stress, displacement, and buckling, and are considered in the optimum design. A comprehensive assessment of the NCSBO on various benchmark design examples is carried out and compared with state-of-the-art methods published in the literature. The results demonstrate the NCSBO algorithm’s superiority in achieving the optimum design, and statistical findings support its consistent ability to obtain competitive solutions.

This paper introduces a novel hybrid metaheuristic optimization algorithm, combining improved formulations of the Slime Mould Algorithm (SMA) and the Jaya Algorithm (JA) (HSMJA) with online distributed computing (ODC), referred to as HSMJA-ODC. While HSMJA hybridizes the improved versions of SMA and JA formulations to maximize searchability, ODC significantly reduces the computation time of the optimization process. The proposed HSMJA-ODC algorithm is used for the weight minimization of steel space frames under strength, displacement, and geometric size constraints. The optimization results obtained from three steel frames confirm the efficiency and robustness of the proposed HSMJA-ODC algorithm, which consistently converges on competitively optimized designs in comparison to its rivals. Moreover, distributed computing reduces computation time by more than 80% compared to single-computer implementations.