# Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm
## 1 Introduction
Benchmark problems, namely 24-bar, 20-bar, and 72-bar, were investigated with different variables for sizing and topology problems. The results obtained from this study were compared against other relative studies, and HGP showed a better performance for most of the cases. [PERSON] et al. [17] proposed a new model for the optimization of truss structures with (PSO) and the cultural algorithm (CA). Together, 10-bar, 25-bar, 72-bar, 120-bar, and 160-bar truss structures were used for checking algorithm viability. They reported that the new proposed model was superior or competitive in comparison with other meta-heuristic algorithms. With the inspiration of the Java algorithm (JA), [PERSON] et al. [18] developed a parameter-free java algorithm (PFJA) for sizing/layout problems of truss structures and compared the outputs with the standard java algorithm (JA), the modified java algorithm (MJA), and other meta-heuristic algorithms in the literature. Optimization was conducted on 37-bar, 52-bar, 72-bar, 200-bar, 942-bar, 600-bar, 1180-bar, and 1410-bar truss structures. With eight different truss weight optimization problems studied in this paper, it is stated that PFJA is a reliable algorithm in terms of minimizing truss weight and convergence speed. For size/shape optimization, [PERSON] et al. [19] applied chaos game optimization with frequency constraints. Five different truss structures such as 10-bar, 37-bar, 52-bar, 72-bar, and 120-bar structures were considered. They reported that with the mean of 30 independent runs, chaos game optimization (CGO) reached the minimum truss weight among the algorithms considered in this study. In another recent study, [PERSON] et al. [20] employed the artificial bee colony algorithm (ABC) for 15-bar, 18-bar, 25-bar, and 47-bar truss structures for sizing and layout optimization. Stress, displacement and buckling constraints were considered. The authors reported that the ABC algorithm outperformed other algorithms with regard to weight and iteration. [PERSON] et al., 2021, focuses on four objective functions: mass, compliance, first natural frequency, and buckling factor [21]. Due to the limited availability of optimization methods designed for addressing many-objective truss-optimization challenges, it becomes crucial to evaluate the performance of contemporary algorithms on these complex problems. This research contributes by examining the relative effectiveness of eighteen established algorithms across various dimensions. The assessment is based on four metrics, aiming to solve intricate truss problems with many objectives. Statistical analysis is conducted considering the best mean and standard deviation outcomes of the objective functions, along with [PERSON]'s rank test. The overall comparison identifies MMIPDE as the top-performing algorithm, with SHAMODE utilizing a whale-optimization approach and the standard SHAMODE as the runners-up. [PERSON] et al. [22] inspired by the imperialist competitive algorithm (ICA), cellular automata (CA), and the dolphin echolocation (DE) algorithm, introduced a new algorithm abbreviated as CA-ACEA. They dictated that the proposed algorithm outperforms other algorithms considered in this study for weight minimization. [PERSON] et al. [23] applied the chaotic coyote algorithm (COA) for the 52-bar, 72-bar, 120-bar, and 200-bar for truss optimization to minimize the structure weight. They also proposed a modified model of the chaotic coyote algorithm (MCOA) and reported that compared to the original COA, the newly proposed algorithm showed competitive results. [PERSON] and [PERSON] [24] aimed to minimize the weight of the trusses with dynamic constraints and applied various meta-heuristic algorithms. Five different truss-optimization problems were considered to evaluate the performance of these algorithms. They reported that with different benchmark problems, the best algorithm to minimize the weight of the truss changed case to case. [PERSON] [25] proposed a meta-heuristic algorithm based on a job-search strategy.