## 1 Introduction  
Various evolutionary optimization methods have been developed by drawing inspiration from natural phenomena such as physics, biology, human social behavior, astronomy, and evolutionary theory. The main aim of these methods is to improve the search process while minimizing computational costs to achieve global optimization. Metaheuristic optimization algorithms are powerful computational methods for solving engineering design problems. The number of metaheuristic optimization methods was relatively limited until the early 2010s. The most important ones in this period are the following: genetic algorithms [1], simulated annealing (SA) [2], tabu search [3], particle swarm optimization (PSO) [4], ant colony optimization (ACO) [5], harmony search (HS) [6], the big bang-big crunch algorithm (BB-BC) [7], the artificial bee colony (ABC) algorithm [8], cuckoo search (CS) [9], and teaching-learning-based optimization (TLBO) [10]. Since the early 2010s, there has been a significant increase in interest in metaheuristic optimization methods, leading to a tremendous rise in the number of novel methods proposed. Among these methods, some of the most notable ones are the following: the grey wolf optimizer (GWO) [11], moth-flame optimization (MFO) [12], the crow search algorithm [13], the Jaya algorithm (JA) [14], the butterfly optimization algorithm (BOA) [15], Henry gas solubility optimization (HGSO) [16], the equilibrium optimizer (EO) [17], the slime mold algorithm [18], the Aquila optimizer (AO) [19], the white shark optimizer [20], circulatory-system-based optimization (CSBO) [21], and the gazelle optimization algorithm (GOA) [22]. Due to the impossibility of citing hundreds of methods in this paper, a thorough comparison of various metaheuristic optimization techniques can be found in recent review papers [23; 24; 25; 26; 27]. Metaheuristic methods have been used to find optimal designs for various engineering problems. Structural optimization, involving many design variables and constraints, is a complicated task that researchers have studied for a long time. The optimum design of truss structures is a challenging problem, and efficient optimization algorithms have been implemented to solve this problem. Notable contributions to this topic include the following papers. [PERSON] and [PERSON] [28] utilized genetic algorithms to optimize the designs of trusses. The method included discrete variables for size optimization and continuous variables for shape refinement. [PERSON] and [PERSON] [29] subsequently improved upon this approach by incorporating genetic algorithms, the finite element method (FEM), and domain-specific knowledge to optimize bridge steel trusses. [PERSON] and [PERSON] [30] pursued layout optimization by utilizing an improved charged system search algorithm (CSS), further enriching the toolbox of optimization techniques. [PERSON] and [PERSON] [31] explored the potential of metaheuristic algorithms, specifically harmony search (HS) and the firefly algorithm (FA), to tackle the complicated optimization of steel trusses by considering size and geometry optimization while accommodating dynamic constraints. [PERSON] et al. [32] engaged the modified big bang-big crunch algorithm to confront the size and shape optimization challenges for truss structures subjected to dynamic excitations. [PERSON] et al. [33] applied the teaching-learning-based optimization (TLBO) method with dynamic constraints for the shape and size optimization of truss structures. [PERSON] et al. [34] proposed a measure-based evolutionary optimization method change by implicitly taking buckling constraints into the truss structures. [PERSON] et al. [35] introduced a novel approach for optimizing truss structures with multi-objective criteria using the multi-objective water cycle algorithm (MOWCA). This algorithm is inspired by real-world water cycle dynamics and demonstrates promising performance in navigating complicated optimization landscapes. [PERSON] et al.