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3D steel truss structure with 260 bars and 76 nodes. The whole decision variables include 10 shape and 260 sizing elements. The shape variables show by Ci and total number of decision variables is 270.
Source publication
In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, mu...
Citations
... The research results showed that MPA had effective performance to solve engineering problems. Recently, Etaati et al. [50] conducted comparative research to apply 12 modern bio-inspired methods to two large-scale truss optimisation problems. The results showed that MPA outperformed other algorithms with regard to convergence rate and efficiency. ...
... Two case studies proposed by Bright Optimiser ISCSO 2018 and 2019 [61]. We have specifically chosen these two truss problems due to their unique characteristics and the limited amount of existing research [50,62] conducted on them. Both problems exhibit non-linearity, non-convexity, complexity, and multimodality, which distinguishes them from the commonly studied truss structures [13][14][15]21]. ...
... It is worth mentioning that in this study, our primary objective is not to directly compare the results obtained by our proposed algorithms with the most recent MPAs available. Instead, our focus is to advance beyond the achievements of our previous research [50] by introducing novel MPAs designed to excel in addressing the challenges posed by the two truss problems. Eventually, we compare the results obtained by the proposed MPAs with our recently proposed adaptive chaotic MPA [62]. ...
Optimising the shape and size of large-scale truss frames is challenging because there is a nonlinear interaction between cross-sectional and nodal coordinate forces of structures. Meanwhile, combining the shape and bar size variables creates a multi-modal search space with dynamic constraints, making an expensive optimisation engineering problem. Besides, most of the real truss problems are large-scale, and optimisation algorithms are faced with the issue of scalability by increasing the size of the problem. This paper proposed a novel Cooperative Coevolutionary marine predators algorithm combined with a greedy search (CCMPA-GS) for truss optimisation on shape and sizing. The proposed algorithm used the divide-and-conquer technique to optimise the shape and size separately. Therefore, in each iteration, the CCMPA-GS focuses on shape optimisation initially and then switches to the size of bars and tries to find the best cooperative combination of the solutions in the current population using a context vector (CV). A greedy search is embedded in the following to fix the remaining violations from the structure's stress and displacement. This novel alternative optimisation strategy (CCMPA-GS) compared with 13 established genetic, evolutionary, swarm, and memetic meta-heuristic optimisation algorithms. The comparison is based on optimising two large-scale truss structures consisting of 260-bar and 314-bar configurations. Experimental results demonstrate that the proposed CCMPA-GS method consistently outperforms the other meta-heuristic methods, delivering optimal designs for the 314-bar and 260-bar truss structures that are superior by 52 % and 63.4 %, respectively. This signifies a substantial enhancement in optimisation performance, highlighting the potential of CCMPA-GS as a powerful alternative in the field of structural optimisation.
... Recent studies on evolutionary optimization algorithms that use the ISCSO optimization problems [26,[28][29][30]32] clearly demonstrate the challenging nature of the ISCSO optimization problems and render them potential benchmarks for performance evaluation of structural optimization techniques. Albert and Zhang [26] proposed SpartaPlex, as a novel black-box optimization algorithm, and employed two challenging approximately 24% and 29% lighter than the best results obtained across all trials and formulations presented in [26]. ...
... Although the foregoing modified version of the ISCSO 2019 problem forms a large-scale practical test example, it does not fully reflect the difficulty of the original version basically due to the reduced number of loading conditions as well as the smaller number of design variables in the modified version. Etaati et al. [28] proposed an optimization framework composed of 12 For structural optimization applications, Kaveh and Biabani Hamedani [30] proposed an improved version of a newly developed metaheuristic method called arithmetic optimization algorithm (AOA) [31]. The authors demonstrated successful performance of the developed improved variant of the arithmetic optimization algorithm (IAOA) on conventional structural optimization benchmarks. ...
Benchmarking is an essential part of developing efficient structural optimization techniques. Despite the advent of numerous metaheuristic techniques for solving truss optimization problems, benchmarking new algorithms is often carried out using a selection of classic test examples which are indeed unchallenging for contemporary sophisticated optimization algorithms. Furthermore, the limited optimization results available in the literature on new test examples are usually not accurately comparable. This is typically due to the lack of infromation about the performance of the investigated algorithms and the inconsistencies between the studies in terms of adopted test examples for benchmarking, optimization problem formulation, maximum number of objective function evaluations and other similar issues. Accordingly, there exists a need for developing new standard test suites composed of easily reproducible challenging test examples with rigorous and comparable performance evaluation results of algorithms on these test suites. To this end, the present work aims to propose a new baseline for benchmarking structural optimization algorithms, using a set of challenging sizing and shape optimization problems of truss structures selected from the international student competition in structural optimization (ISCSO) instances. The most recent six structural optimization examples from the ISCSO are tackled using a representative metaheuristic structural optimization algorithm. The statistical results of all the optimization runs using the proposed benchmarking suite are provided to pave the way for more rigorous benchmarking of structural optimization algorithms.
... The research results showed that MPA had effective performance to solve engineering problems. Recently, Etaati et al. [42] conducted comparative research to apply 12 modern bio-inspired methods to two large-scale truss optimisation problems. The results showed that MPA outperformed other algorithms with regard to convergence rate and efficiency. ...
Optimising the shape and size of large-scale truss frames is challenging because there is a nonlinear interaction between cross-sectional and nodal coordinate forces of structures. Meanwhile, combining the shape and bar size variables makes a multi-modal search space with dynamic constraints that makes an expensive optimisation problem. Besides, most of the real truss problems are large-scale, and optimisation algorithms are faced with the issue of scalability by increasing the size of the problem. This paper proposed a novel Cooperative Coevolutionary marine predators algorithm combined with a greedy search (CCMPA-GS) for truss optimisation on shape and sizing. The proposed algorithm used the divide-and-conquer technique to optimise the shape and size separately. Therefore, in each iteration, the CCMPA-GS focuses on shape optimisation initially and then switches to the size of bars and tries to find the best cooperative combination of the solutions in the current population using a context vector (CV). The CV holds the best-found configuration of the shape and sizing values. In the following, a greedy search is embedded to fix the remaining violations from the structure's stress and displacement. This alternative optimisation strategy is compared with 13 well-known genetic, evolutionary, swarm and memetic meta-heuristics using two large-scale 260-bar and 314-bar truss structures. The experimental findings indicate that the proposed optimisation method outperforms other meta-heuristic methods considerably.
... Lévy flight techniques (intrinsic to DA operations) proved crucial to the algorithm's excellent performance, allowing it to reach global truss solutions with the least computational effort. The results were furthermore corroborated with a similar metaheuristic truss optimization study conducted by Etaati et al. [30]. Along those lines, other studies related to the field have also reported the advantage that Lévy-based MH algorithms possess over other techniques for truss optimization problems (see Refs. [31][32][33][34][35]). ...
In this study, the newly developed Marine Predators Algorithm (MPA) is formulated to minimize the weight of truss structures. MPA is a swarm-based metaheuristic algorithm inspired by the efficient foraging strategies of marine predators in oceanic environments. In order to assess the robustness of the proposed method, three normal-sized structural benchmarks (10-bar, 60-bar, and 120-bar spatial dome) and three large-scale structures (272-bar, 942-bar, and 4666-bar truss tower) were selected from the literature. Results point to the inherent strength of MPA against all state-of-the-art metaheuristic optimizers implemented so far. Moreover, for the first time in the field, a quantitative evaluation and an answer to the age-old question of the proper convergence behavior (exploration vs. exploitation balance) in the context of structural optimization is conducted. Therefore, a novel dimension-wise diversity index is adopted as a methodology to investigate each of the two schemes. It was concluded that the balance that produced the best results was about 90% exploitation and 10% exploration (on average for the entire computational process).
The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it’s called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.
Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.
