Multiobjective optimization of large-scale structures

Abstract

This paper presents a multiobjective optimization algorithm based on generalized compound scaling techniques. The algorithm handles any number of objective functions, similar to handling behavior constraints. This technique generates a partial Pareto set while solving the optimization problem. A reliability-based decision criterion is used for selecting the best compromise design. The example cases considered in this work include various disciplines in airframe structures, such as stress, displacement, and frequency with hundreds of design variables and constraints. This paper also discusses the concept of Pareto-optimal solutions in the context of a multiobjective structural optimization problem and the commonly used methods of generating Pareto-optimal solutions.

Multiobjective Shape Optimization for Deployment and Adjustment Properties of Cable-Net of Deployable Antenna

“…We use weighting coefficients method [12][13][14] to deal with the multiobjective problem here. In this method, all the objectives are multiplied by different weighting coefficients separately, and then they are added together to form a single objective that can be solved by conventional optimization methods:…”

Vulcanizing Process Parameter Robust Optimization Based on an Improved Genetic Algorithm

“…In fact, with iteration increasing group of individual differences will be gradually reduced, then the actual operation is repeated cross-breeding and cross close relatives, and the smaller the mutation rate cannot effectively get rid of the groups hyperplane, would not achieve the purpose of maintaining diversity of the sample space. In this paper, an adaptive strategy based on the temperature was used to crossover and mutation operators [7].…”

“…According to principle, the robust optimization design can be divided into two categories: one category is based on the experimental design of robust optimal design, namely the traditional robust design optimization, such as the main S / N ratio [5], response surface method [6] and, method of loss function [7], the other is modern robust optimal design which is based on the mathematical model of robust optimal design, has mainly the weight method [8], compatibility decision making problems [9] and physical programming [10]. The first class of method is very effective to the test design, but S / N ratio of in the actual engineering is not easy to obtain, and cannot establish a strict mathematical model for robust design; the second kind of method is based on the computer aided engineering (CAD)and nonlinear programming foundation, mathematical modeling is relatively easy, and the system can be reliability into robust design.…”

Multilevel Structural Optimization for Preliminary Wing-Box Weight Estimation

“…These considerations also apply to unconventional aircraft, for example, strut-braced wings, joined wings, and the PrandtlPlane® configuration, described in [3], that need very sophisticated design tools [4,5] and for which meaningful statistical data are not available. The aircraft global optimization needs a reliable estimate of the wing weight and should account for any kind of interaction with the wing structural design, for example, the center of gravity position.…”