Structural Engineering Design Problems Research Articles

Chaotic Social Group Optimization for Structural Engineering Design Problems

Chaotic Social Group Optimization for Structural Engineering Design Problems

LX-BBSCA: Laplacian biogeography-based sine cosine algorithm for structural engineering design optimization

<abstract> <p>In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA and LX-BBO but also other state-of-the-art metaheuristic algorithms.</p> </abstract>

Design of buckling restrained steel braces using application programming interface between simulation and discrete optimization

In this study, buckling restrained braces (BRBs) are optimized through application programming interface between simulation and discrete optimization. It is aimed to maximize the energy dissipation capacity of BRBs considering the American Institute of Steel Construction (ANSI/AISC 341). Unlike other studies, BRBs modeled in the finite element packaged software are directly linked to optimization algorithms. So, the geometric and material nonlinearities are also considered. Harmony Search Algorithm (HSA), which simulates the improvisation musician performances in finding pleasing harmony, and Black Widow Optimization Algorithm (BWOA), which imitates inimitable paring attitude of black widow spiders, are taken as the optimizer tools of this study. They encoded in Microsoft Visual Basic programming language. Initially, the algorithmic performances of the HSA and BWOA are compared and evaluated on two benchmark structural engineering design problems. Afterward, two different shaped BRBs are modeled in a finite element analysis (FEA) based software, namely ANSYS Workbench. Then, the obtained simulations are integrated with the HS and BWO algorithms throughout the application programming interface without identification of complex objective function and design constraints in formulations. These are directly calculated by ANSYS Workbench over very simple formulas. So, the attained optimum designs of BRBs are investigated with a new approach. Furthermore, in order to see the supreme algorithmic performances of the HSA and BWOA, all benchmark and BRB design problems are solved so-called well-established conventional standard Genetic Algorithm (sGA). Eventually, the proposed novel design methodology gives opportunity and eases to the designers since it provides convenience in solving complicated problems having nonlinear objective function and design constraints that are tiresome to encode.

Multiobjective forensic-based investigation algorithm for solving structural design problems

A multi-objective forensic-based investigation (MOFBI) algorithm is developed to solve engineering optimization problems with multiple objectives. In the proposed algorithm, a chaotic map is used to initialize the population; Lévy flight, two elite populations, and a fixed-size archive are used to operate the motions of investigators and police officers in the criminal search and arrest procedures, and a control time mechanism is used to balance exploration and exploitation in MOFBI to obtain Pareto-optimal solutions in multi-objective search spaces. Eight well-known multiple-objective metaheuristic optimization algorithms - the multi-objective ant lion optimizer (MOALO), the multi-objective dragonfly algorithm (MODA), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective grey wolf optimizer (MOGWO), the multi-objective particle swarm optimization (MOPSO), the fast and elitist multi-objective genetic algorithm (NSGA-II), pareto envelope-based selection algorithm II (PESA-II) and the strength pareto evolutionary algorithm II (SPEA-II) - are compared to MOFBI with respect to performance in solving 24 multi-objective mathematical benchmark problems (CEC-2020 functions). The Wilcoxon rank sum test of hypervolume index, generational distance and spacing reveal that MOFBI can find more accurate approximations of Pareto-optimal fronts than the other algorithms. MOFBI is then used to solve five structural engineering design problems, including the 10-bar truss, the 72-bar truss, the 56-bar dome, the 120-bar dome and the 582-bar tower. The results obtained using MOFBI and comparison thereof with previously obtained results indicate the effectiveness of MOFBI in finding the best Pareto-optimal solutions. Therefore, MOFBI is a powerful computer-aided tool for solving multi-objective optimization problems.

Design of an efficient hybridized CS-PSO algorithm and its applications for solving constrained and bound constrained structural engineering design problems

In the recent years, studies of structural engineering design problems are of keen interest among the researchers including engineers and algorithm developers. This article attempts to design an efficient hybridized algorithm for solving structural engineering design optimization problems. In this algorithm, after imposing some modifications on cuckoo search algorithm, the whole swarm or population is partitioned into two groups and then these are upgraded by the embedded cuckoo search (ECS) and a variant of PSO (AGQPSO) algorithms. After combining these two upgraded swarms, a deletion strategy of CS is applied with certain effective modifications. The performance of this hybridized algorithm is tested by considering six structural engineering design problems reported by several researchers and the errors made by some of them are pointed out. Finally, a comparison of the best found results and statistical analyses of this crossbred algorithm are performed with the same of existing algorithms available in the literature and it is shown that the presented algorithm performs better in almost all aspects. Also, to establish the efficiency of the proposed algorithm, convergence graphs and statistical tests are provided.

Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications

Abstract This paper focuses on a comparision of recent algorithms such as the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm. The slime mold algorithm (SMA) is a recent optimization algorithm. In order to strengthen its exploitation and exploration abilities, in this paper, a new hybrid slime mold algorithm-simulated annealing algorithm (HSMA-SA) has been applied to structural engineering design problems. As a result of the rules and practices that have become mandatory for fuel emissions by international organizations and governments, there is increasing interest in the design of vehicles with minimized fuel emissions. Many scientific studies have been conducted on the use of metaheuristic methods for the optimum design of vehicle components, especially for reducing vehicle weight. With the inspiration obtained from the above-mentioned methods, the HSMA-SA has been studied to solve the shape optimization of a design case to prove how the HSMA-SA can be used to solve shape optimization problems. The HSMA-SA provides better results as an arithmetic optimization algorithm than the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm.

Nature-inspired approach: An enhanced whale optimization algorithm for global optimization

The whale optimization algorithm is based on the bubble-net attacking behavior of humpback whales and simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. However, the basic whale optimization algorithm has the disadvantage of search stagnation, easily falls into a local optimum, has slow convergence speed and has low calculation accuracy. The Lévy flight strategy is beneficial for expanding the search range and prevents the algorithm from falling into a local optimum, which enhances the global search ability. The ranking-based mutation operator can increase the selection probability and accelerate the convergence speed to enhance the local search ability. To overcome these shortcomings and avoid premature convergence, the Lévy flight strategy and the ranking-based mutation operator are added to the whale optimization algorithm. In this paper, an enhanced whale optimization algorithm is proposed, which realizes complementary advantages to balance exploration and exploitation. Eighteen benchmark test functions and five structural engineering design problems are used to verify the robustness and overall optimization performance of the enhanced whale optimization algorithm. The experimental results show that the enhanced whale optimization algorithm is an effective and feasible method that has a fast convergence speed, high calculation accuracy, strong robustness and stability.

Optimal Solution of Structural Engineering Design Problems using Crow Search Algorithm

In real world, the structural engineering design problems are large scale non-linear constrained problems. In the present study, crow search algorithm (CSA) is applied to find the optimal solution of structural engineering design problems such as pressure vessel design problem, welded beam design problem and tension/ compression string design problem. The numerical results are compared with the existing results reported in the literature including metaheuristic algorithms and it is found that the results obtained by the crow search algorithm are better than other existing algorithms. Further, the effectiveness of the algorithm is verified to be better than the existing algorithms by statistical analysis using mean, median, best case, and worst case scenarios. The present study confirms that the crow search algorithm may be easily and effectively applied to various structural design problems.

Different Discrete ACCS Algorithms for Optimal Design of Truss Structures: A Comparative Study

Many optimization techniques based on swarm intelligence have been developed for size optimization of skeletal structures. Cuckoo search algorithm, artificial bee colony algorithm, colliding bodies optimization, artificial coronary circulation system (ACCS) are only some examples of these algorithms. In these methods, the sizing variables are often assumed to be continuous. However, in most practical structural engineering design problems, the design variables are discrete. The aim of this study is to present an optimization algorithm based on the ACCS algorithm for the discrete optimum design of truss structures. Here, the discrete search strategy using the ACCS algorithm is presented by three different scenarios in detail and their effectiveness and robustness are compared to those of the recently developed discrete optimization methods. The ACCS optimization algorithm uses the visual center point of populations in each iteration and simulates the process of growing coronary arteries of heart. Finally, it is shown that the discrete ACCS has the fastest convergence rate among the considered algorithms and can be effectively used to solve optimal design of truss structures with discrete variables.

Stable equilibrium configuration of two bar truss by an efficient nonmonotone global Barzilai–Borwein gradient method in a fuzzy environment

The stable equilibrium configuration of structures is a main goal in structural optimization. This goal may be achieved through minimizing the potential energy function. In the real world, sometimes, the input data and parameters of structural engineering design problems may be considered as fuzzy numbers which lead us to develop structural optimization methods in a fuzzy environment. In this regard, the present paper is intended to propose a fuzzy optimization scheme according to the nonmonotone globalization technique, the Barzilai–Borwein (BB) gradient method and the generalized Hukuhara differentiability (gH-differentiability). In fact, using the best benefits of BB-like methods i.e., simplicity, efficiency and low memory requirements, a modified global Barzilai–Borwein (GBB) gradient method is proposed for obtaining a non-dominated solution of the unconstrained fuzzy optimization related to the two bar asymmetric shallow truss in a fuzzy environment. The global convergence to first-order stationary points is also proved and the R-linear convergence rate is established under suitable assumptions. Furthermore, some numerical examples are given to illustrate the main results.

A novel fuzzy adaptive teaching–learning-based optimization (FATLBO) for solving structural optimization problems

This paper presents a new optimization algorithm called fuzzy adaptive teaching---learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching---learning-based optimization namely status monitor, fuzzy adaptive teaching---learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems.

Symbiotic Organisms Search: A new metaheuristic optimization algorithm

This paper applies a new robust and powerful metaheuristic algorithm called Symbiotic Organisms Search (SOS) to numerical optimization and engineering design problems. SOS simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. Twenty-six unconstrained mathematical problems and four structural engineering design problems are tested and obtained results compared with other well-known optimization methods. Obtained results confirm the excellent performance of the SOS method in solving various complex numerical problems.

The harmony search heuristic algorithm for discrete structural optimization

Many methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this article, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through several standard truss examples. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current methods.

Hopfield neuron based nonlinear constrained programming to fuzzy structural engineering optimization

Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

A Microcomputer Interactive Graphics Software for Design of Steel Trusses

Current advances in microcomputer technology provide the tools to use interactive graphics in the development of efficient computer programs. The use of computer graphics facilitates the definition of complicated engineering models, and permits a clear display of the results. In this paper, an educational microcomputer software package, TRUSS‐AD, is described to demonstrate the effectiveness of applying interactive computer graphics in solving structural engineering design problems. TRUSS‐AD allows the user to: (1) define a truss geometry and loading conditions graphically; (2) analyze and design the truss in an interactive mode; and (3) display all results in a concise and clear graphical representation. The stiffness method is used in the analysis process. The design can be performed by using either the “Load and Resistance Factor Design” or the “Allowable Stress Design” methods. The software is written using the basic language for IBM or compatible personal computers.