Weight Minimization Of Truss Structures Research Articles

Weight minimization of truss structures using an improved Harris hawks optimization algorithm

Weight minimization of truss structures using an improved Harris hawks optimization algorithm

Critical Evaluation of Metaheuristic Algorithms for Weight Minimization of Truss Structures

This study critically compares variants of Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony, Differential Evolution and Simulated Annealing used in truss sizing optimization problems including displacement and stress constraints. The comparison is based on several benchmark problems of varying complexity number of design variables. i.e. the number of design variables, and the degree of static indeterminacy. Most of these problems have been studied by numerous researchers using a large variety of methods; this allows for absolute rather than relative comparison. Rigorous statistical analysis based on large sample size, as well as monitoring of the success rate throughout the optimization process, reveal and explain the convergence behavior observed for each method. The results indicate that, for the problem at hand, Differential Evolution is the best algorithm in terms of robustness, performance, and scalability.

Algoritmo simulated annealing modificado para diseño óptimo de armaduras con variables continuas

<p class="p1">En los últimos años, la importancia de los aspectos económicos en el campo de las estructuras ha motivado a muchos investigadores a proponer nuevos métodos para minimizar el peso de las estructuras. En este trabajo, el algoritmo simulated annealing modificado (ASAM), es presentado para resolver la optimización de peso en armaduras con variables continuas. Para evaluar y validar el desempeño de ASAM se abordaron cinco problemas reportados en la literatura especializada. Los resultados de ASAM en comparación con los reportados por otros algoritmos de optimización, muestran que este algoritmo puede generar diseños mejorados y ser utilizado efectivamente en la minimización de peso en armaduras.</p>

Performance evaluation of local surrogate models in differential evolution-based optimum design of truss structures

PurposeThe purpose of this paper is to propose and analyze the use of local surrogate models to improve differential evolution’s (DE) overall performance in computationally expensive problems.Design/methodology/approachDE is a popular metaheuristic to solve optimization problems with several variants available in the literature. Here, the offspring are generated by means of different variants, and only the best one, according to the surrogate model, is evaluated by the simulator. The problem of weight minimization of truss structures is used to assess DE’s performance when different metamodels are used. The surrogate-assisted DE techniques proposed here are also compared to common DE variants. Six different structural optimization problems are studied involving continuous as well as discrete sizing design variables.FindingsThe use of a local, similarity-based, surrogate model improves the relative performance of DE for most test-problems, specially when using r-nearest neighbors with r = 0.001 and a DE parameter F = 0.7.Research limitations/implicationsThe proposed methods have no limitations and can be applied to solve constrained optimization problems in general, and structural ones in particular.Practical/implicationsThe proposed techniques can be used to solve real-world problems in engineering. Also, the performance of the proposals is examined using structural engineering problems.Originality/valueThe main contributions of this work are to introduce and to evaluate additional local surrogate models; to evaluate the effect of the value of DE’s parameter F (which scales the differences between components of candidate solutions) upon each surrogate model; and to perform a more complete set of experiments covering continuous as well as discrete design variables.

Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm

Natural frequencies offer useful knowledge on the dynamic response of the structures. It is possible to avoid from the destructive effects of dynamic loads on the structures by optimizing layout and size of their subject to constraints on natural frequencies. Since optimization problems including frequency constraints are highly nonlinear, this kind of problems forms a challenging area to test the performance of the different optimization techniques. This study tests the performance of an integrated particle swarm optimization algorithm (iPSO), a new particle swarm optimizer integrated with the improved fly-back mechanism and the weighted particle concept, in four weight minimization of truss structures with sizing and layout variables under multiple frequency constraints. Optimization results demonstrate that the new algorithm is competitive with other state-of-the-art metaheuristic algorithms in dynamic and static structural optimization problems.

Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas

The aim of this study is to use stochastic optimization algorithm MSAA (Modified Simulated Annealing Algorithm) for trusses plane optimization (weight minimization) with discrete variables. MSAA is based on the cooling process of metal used in the Simulated Annealing (SA) classic, but it has three fundamental characteristics (preliminary exploration, search step and acceptance probability) that differentiate this. To evaluate and validate the MSAA performance were studied three problems plane trusses weight minimization with discrete variables reported in the literature and the results are compared with those obtained by other authors using different optimization algorithms. It is concluded that the MSAA algorithm presented in this study can be effectively used in the weight minimization of truss structures.

Simultaneous size, shape, and topology optimization of truss structures using integrated particle swarm optimizer

The current investigation deals with the weight minimization of truss structures accomplishing the simultaneous shape, size, and topology optimization. In this regard, this study presents an effect...

Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures

This paper presents the applications of the mine blast algorithm (MBA) and the water cycle algorithm (WCA), in addition to an improved version of MBA for weight minimization of truss structures including discrete sizing variables. The MBA mimics the explosion of landmines, while the WCA is inspired by the observation of water cycle process. An improved version of MBA (IMBA), is also presented. The efficiency of the three optimization algorithms is tested using classical benchmark discrete truss design problems. Optimization results show that MBA, IMBA, and WCA offer a good degree of competitiveness against other state-of-the-art metaheuristic techniques.

Application of Teaching-Learning-Based-Optimization algorithm for the discrete optimization of truss structures

The aim of this study is to present a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO). The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several benchmark problem related truss structures with discrete design variables are used to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

Structural optimization with teaching-learning-based optimization algorithm

In this paper, a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO) is used for the least weight design of trusses with continuous design variables. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several truss structures are analyzed to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

Weight minimization of trusses with genetic algorithm

This study is concerned with encoding types such as value encoding and binary encoding for continuous and discrete optimization with a GA, which is coded in FORTRAN and considers stress and displacement constraints, in view of weight minimization of truss structures. Moreover, when continuous optimization is performed, the challenge of huge search space due to the effort of considering continuous set of design variables is overcame by a mechanism introduced as restricted range approach (RRA) in this study. In comparison with the literature, it is concluded that the program developed in this study can be effectively used in the weight minimization of truss structures. It is also came to the conclusion that value encoding overcomes the adverse effects of Hamming-cliff, and that value encoding requires less computer memory and time, never destroys the fit chromosome.