Study of evolutionary structural optimization and applications (original) (raw)

Evolutionary structural optimization for stress minimization problems by discrete thickness design

Computers & Structures, 2000

Stress minimization is a major aspect of structural optimization in a wide range of engineering designs. This paper presents a new evolutionary criterion for the problems of variable thickness design whilst minimizing the maximum stress in a structure. On the basis of ®nite element analysis, a stress sensitivity number is derived to estimate the stress change in an element due to varying the thickness of other elements. Following the evolutionary optimization procedure, an optimal design with a minimum maximum stress is achieved by gradually removing material from those elements, which have the lowest stress sensitivity number or adding material onto those elements, which have the highest stress sensitivity number. The numerical examples presented in this paper demonstrate the capacity of the proposed method for solving stress minimization problems. The results based on the stress criterion are compared with traditional ones based on a stiness criterion, and an optimization scheme based on the combination of both the stress minimization and the stiness maximization criteria is presented. Ó

Comparing the Fully Stressed Design and the Minimum Constrained Weight Solutions in Truss Structures

Computational methods in applied sciences, 2015

The optimization structural design problems of Fully Stressed Design (FSD) and Minimum Constrained Weight (MCW) are compared in this work in a simple truss test case with discrete cross-section type bar sizing, where both optimum designs are coincident. An analysis of the whole search space is included, and the optimization behaviour of evolutionary algorithms are compared with multiple population sizing and mutation rates in both problems. Results of average, best and standard deviation metrics indicate the success and the robustness of the methodology, as well as the fastest and easiest behaviour when considering the FSD case.

A SIMPLE EVOLUTIONARY PROCEDURE FOR STRUCTURAL OPTIMIZATION

A simple evolutionary procedure is proposed for shape and layout optimization of structures. During the evolution process low stressed material is progressively eliminated from the structure. Various examples are presented to illustrate the optimum structural shapes and layouts achieved by such a procedure.

A smooth evolutionary structural optimization procedure applied to plane stress problem

Engineering Structures, 2014

Topological optimization problems based on stress criteria are solved using two techniques in this paper. The first technique is the conventional Evolutionary Structural Optimization (ESO), which is known as hard kill, because the material is discretely removed; that is, the elements under low stress that are being inefficiently utilized have their constitutive matrix has suddenly reduced. The second technique, proposed in a previous paper, is a variant of the ESO procedure and is called Smooth ESO (SESO), which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it no longer influences the structure; its removal is thus performed smoothly. This procedure is known as ''soft-kill''; that is, not all of the elements removed from the structure using the ESO criterion are discarded. Thus, the elements returned to the structure must provide a good conditioning system that will be resolved in the next iteration, and they are considered important to the optimization process. To evaluate elasticity problems numerically, finite element analysis is applied, but instead of using conventional quadrilateral finite elements, a planestress triangular finite element was implemented with high-order modes for solving complex geometric problems. A number of typical examples demonstrate that the proposed approach is effective for solving problems of bi-dimensional elasticity.

Application of Evolutionary Optimization in Structural Engineering

IFIP Advances in Information and Communication Technology, 2009

Practical optimization methods including genetic algorithms are introduced, based on evolutionary computing or soft computing. Several application examples are presented to demonstrate and discuss the efficiency and applicability of the described methods.

Comparison of evolutionary-based optimization algorithms for structural design optimization

In this paper, a comparison of evolutionary-based optimization techniques for structural design optimization problems is presented. Furthermore, a hybrid optimization technique based on differential evolution algorithm is introduced for structural design optimization problems. In order to evaluate the proposed optimization approach a welded beam design problem taken from the literature is solved. The proposed approach is applied to a welded beam design problem and the optimal design of a vehicle component to illustrate how the present approach can be applied for solving structural design optimization problems. A comparative study of six population-based optimization algorithms for optimal design of the structures is presented. The volume reduction of the vehicle component is 28.4% using the proposed hybrid approach. The results show that the proposed approach gives better solutions compared to genetic algorithm, particle swarm, immune algorithm, artificial bee colony algorithm and differential evolution algorithm that are representative of the state-of-the-art in the evolutionary optimization literature.

A Genetic Evolution Algorithm for Structural Optimization

The application of genetic algorithm-based methodology for the structural design is presented in this study. The genetic algorithm is used to design prestressed concrete beams (PCB). The target objective in this method is to obtain set of optimal geometrical dimensions of symmetrical I-beam cross section. Additionally, the amount of pre-stressing steel is optimized. Post-tensioned prestressed beam with a single duct of parabolic shape is considered in the application. Several parameters are studied including the effect of the span length considering different loading cases. The performance constraints are adopted according to the ACI 318/95 Building Code provisions [1]; including the flexural stresses, the ultimate moment capacity of the section with respect to cracking moment, the maximum crack width, the immediate deflection and the long term deflection in addition to the side constraints. The results are presented and compared; several design charts are developed and presented. The present study showed the promising capabilities of the genetic algorithm in optimal designs, and showed the practicability of the genetic algorithm for different structural optimization problems.

Structural optimization using evolutionary algorithms

Computers & Structures, 2002

The objective of this paper is to investigate the efficiency of various evolutionary algorithms (EA), such as genetic algorithms and evolution strategies, when applied to large-scale structural sizing optimization problems. Both type of algorithms imitate biological evolution in nature and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this paper modified versions of the basic EA are implemented to improve the performance of the optimization procedure. The modified versions of both genetic algorithms and evolution strategies combined with a mathematical programming method to form hybrid methodologies are also tested and compared and proved particularly promising. The numerical tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale optimization problems.

Design of structures for optimal static strength using ESO

Engineering Failure Analysis, 2005

This paper presents a modified evolutionary structural optimisation (ESO) algorithm for optimal design of damage tolerant structures. The proposed ESO algorithm uses fracture strength as the design objective. The formulation outlined here can be used for shape optimisation of structures and allows for cracks to be located along the entire structural boundary. In this work we use an approximate method for evaluating the stress intensity factors associated with the cracks. This extended ESO algorithm is illustrated using the problem of the optimal shape design of a 'cutout' in a rectangular plate under biaxial loading and the design of a shoulder fillet under uniaxial tension. It is found that this method reduced the maximum stress intensity factor for the optimum shape and produced a near uniform level of fracture criticality around the boundary. It is also shown that the shapes optimised for stress and fracture strength may differ. This highlights the need to explicitly include fracture parameters in the design objective function. The results agreed well with those reported in the literature using a biological method.