## 1 Introduction
[36] modified a recently developed metaheuristic algorithm called the material generation algorithm (MGA) for the optimum design of three benchmark steel trusses. The results demonstrated that the MGA could provide acceptable designs for the problems. [PERSON] et al. [37] demonstrate the effectiveness of genetic algorithms in optimizing truss structures, bypassing gradient calculations, and exploring vast design spaces efficiently. Their approach combines topological and size optimization, achieving improved weight and stress ratios. [PERSON] et al. [38] presented a modified coyote optimization algorithm (MCOA) for solving structural optimization problems. Comparative analysis with the original COA using four truss examples demonstrated the robustness of MCOA. [PERSON] and [PERSON] [39] investigated the efficacy of the shuffled shepherd optimization algorithm (SSOA) in the layout optimization of truss systems. SSOA, a multi-community algorithm inspired by the behavior of shepherds in nature, is examined across various layout optimization problems, including 15-bar, 18-bar, 25-bar, and 47-bar trusses, as well as a 272-bar transmission tower, demonstrating SSOA's competitive performance. [PERSON] and [PERSON] [40] utilized Rao algorithms [41], a parameter-less novel metaheuristic method, for global optimization, focusing on the weight optimization of truss structures. The algorithm integrates Rao algorithms with feasible boundary search (FBS), ensuring the feasibility of solutions. [PERSON] et al. [42] proposed a hybrid harmony search algorithm, LSSO-HHSJA, tailored for the weight minimization of large-scale truss structures. Combining harmony search with the Jaya method aimed to reduce structural analyses. LSSO-HHSJA utilizes gradient information and Jaya-based operators to refine trial designs, achieving competitive results in large-scale optimization problems. [PERSON] and [PERSON] [43] introduced the artificial coronary circulation system (ACCS), a novel optimization algorithm for the weight optimization of truss structures with continuous variables. The algorithm incorporates heart memory and boundary-handling strategies. ACCS's effectiveness was assessed in solving the weight minimization problems of five truss structures. [PERSON] et al. [44] investigated the comparative performance of fourteen multi-objective metaheuristics in truss optimization, an area less explored in the literature than in single-objective cases. Eight classical trusses serve as test problems. Through comprehensive analysis, the study sheds light on the strengths and weaknesses of each algorithm, aiding in the development of tailored optimization approaches for truss design problems. [PERSON] and [PERSON] [45] examined the effectiveness of the improved vibrating particles system (IVPS) algorithm inspired by the free vibration of viscous-damped systems in optimizing the size and layout of truss structures. Results demonstrate competitive performance against other powerful algorithms, with better outcomes in some cases. [PERSON] [46] explored the application of the political optimizer (PO) algorithm [47] in structural optimization. Results on various truss structures demonstrate the PO's superiority regarding optimized weight, stability, and convergence speed for small- to medium-scale systems. Many papers on the optimization of truss structures are not mentioned here. A comprehensive literature survey on this topic can be found in review papers [48; 49; 50; 51; 52]. Circulatory-system-based optimization (CSBO) [21] is one of the most powerful recently proposed optimization methods. The algorithm achieves a highly effective optimization process by emulating biological circulatory systems. However, its efficiency in truss optimization remains suboptimal, indicating a need for further enhancements to improve overall performance. A new approach to circulatory-system-based optimization (NCSBO) that takes inspiration from how blood flows in the human circulatory system is introduced in this paper.