Evolutionary computation and structural design: A survey of the state-of-the-art

Vehicle crashworthiness is an important issue to ensure passengers safety and reduce vehicle costs in the early design stage of vehicle design. The aim of the crashworthiness design is to provide an optimized structure that can absorb the crash energy by controlled vehicle deformations while maintaining enough space of the passenger compartment. Meta-modeling and optimization techniques have been used to reduce the vehicle design cycle. In this paper, a new particle swarm-based optimization method is presented for multi-objective optimization of vehicle crashworthiness. The proposed optimization method is first validated with a multi-objective disk brake problem taken from literature. Finally, it is applied to multi-objective crashworthiness optimization of a full vehicle model and milling optimization problems to demonstrate its effectiveness and validity.

Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach

Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. Evolutionary computation (EC) is generating considerable interest for solving real world engineering problems. They are proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimisation tools. The core methodologies of EC are genetic algorithms (GA), evolutionary programming (EP), evolution strategies (ES) and genetic programming (GP). This paper attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent EC applications for solving real world problems in the manufacturing industry. The survey outlines the current status and trends of EC applications in manufacturing industry. For each application domain, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of inhibitors to industrial applications of optimisation algorithms.

Evolutionary computing in manufacturing industry: an overview of recent applications

Compliant mechanisms are elastic continua used to transmit or transform force and motion mechanically. The topology optimization methods developed for compliant mechanisms
also give the shape for a chosen parameterization of the design domain with a fixed mesh. However, in these methods, the shapes of the flexible segments in the resulting
optimal solutions are restricted either by the type or the resolution of the design parameterization. This limitation is overcome in this paper by focusing on optimizing the skeletal shape of the compliant segments in a given topology. It is accomplished by identifying
such segments in the topology and representing them using Bezier curves. The vertices of the Bezier control polygon are used to parameterize the shape-design space. Uniform
parameter steps of the Bezier curves naturally enable adaptive finite element discretization of the segments as their shapes change. Practical constraints such as avoiding
intersections with other segments, self-intersections, and restrictions on the available space and material, are incorporated into the formulation. A multi-criteria function from our prior work is used as the objective. Analytical sensitivity analysis for the objective
and constraints is presented and is used in the numerical optimization. Examples are included to illustrate the shape optimization method.

Freeform Skeletal Shape Optimization of Compliant Mechanisms

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples.

Optimal design of functionally graded materials using a procedural model and particle swarm optimization

Structural shape optimization 3D finite-element models based on genetic algorithms and geometric modeling

Finite elements, genetic algorithms and b-splines: a combined technique for shape optimization

The optimization of bidimensional shapes is one of the most commonly addressed problems in engineering. This work is concerned with the use of Genetic Algorithms (GAs) and β-spline-curves modeling for the optimization of Boundary Element Models (BEM). The paper briefly summarizes the basis of the GAs formulation and describes how to use refined genetic operators. The model boundary is discretized by using the BEM, and selected parts of the boundary are modeled by using β-spline curves, in order to allow easy remeshing and adaptation of the boundary to the external actions. Two numerical examples are presented and discussed in detail, showing that the proposed combined technique is able to optimize the shape of the domains with minimum computational effort. The reduction in the model area is significant, without violating the restrictions imposed to the model.

W. Annicchiarico

Papers

Structural shape optimization 3D finite-element models based on genetic algorithms and geometric modeling

Finite elements, genetic algorithms and b-splines: a combined technique for shape optimization

Optimization of 2D boundary element models using β-splines and genetic algorithms

Boundary elements and β-spline surface modeling for medical applications

Optimization of finite element bidimensional models: an approach based on genetic algorithms