Improved method and system in engineering design optimization based on multi-objective evolutionary algorithm

A multi-objective evolution and engineering design technology, applied in the field of engineering design optimization, can solve the problems of Pareto optimal solution without diversification, poor diffusion and uniformity, and insufficient convergence

Active Publication Date: 2011-09-21
LIVERMORE SOFTWARE TECH
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

One of the problems in MOEA is that the resulting Pareto-optimal solution may not be diverse (i.e., the divergence and uniformity may be poor), or the convergence may be insufficient

Method used

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  • Improved method and system in engineering design optimization based on multi-objective evolutionary algorithm
  • Improved method and system in engineering design optimization based on multi-objective evolutionary algorithm
  • Improved method and system in engineering design optimization based on multi-objective evolutionary algorithm

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Embodiment Construction

[0022] first reference Figure 1A , the tubular structural component 102 (ie, a representative engineered product) is optimized in engineering optimization where the design objective is to minimize weight under certain design load conditions to minimize the cost of a specified material (eg, common strength steel). Clearly, a thinner thickness 104 will result in a less weighty structure. However, at a certain point, the structure will become too weak to withstand the design load (such as a failed structure due to material yielding or material curvature). Therefore, engineering optimization of this tubular structure requires another design goal of maximizing strength, which leads to a safer structure. In this representative example, thickness 104 is a design variable that can have a range (eg, from 1 / 8 inch to 1 / 2 inch) as a design space. Any design proposals are selected from within this space. In a multi-objective evolutionary algorithm, each generation of populations or des...

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Abstract

Systems and methods of obtaining a set of better converged and diversified Pareto optimal solutions in an engineering design optimization of a product (e.g., automobile, cellular phone, etc.) are disclosed. According to one aspect, a plurality of MOEA based engineering optimizations of a product is conducted independently. Each of the independently conducted optimizations differs from others withparameters such as initial generation and / or evolutionary algorithm. For example, populations (design alternatives) of initial generation can be created randomly from different seed of a random or pseudo-random number generator. In another, each optimization employs a particular revolutionary algorithm including, but not limited to, Nondominated Sorting Genetic Algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA), etc. Furthermore, each independently conducted optimization's Pareto optimal solutions are combined to create a set of better converged and diversified solutions. Combinations can be performed at one or more predefined checkpoints during evolution process of the optimization.

Description

technical field [0001] The present invention relates to engineering design optimization, more specifically, relate to a kind of Pareto (Pareto) global optimal solution that obtains a group of better convergence and diversification in the engineering design optimization based on multi-objective evolution algorithm (MOEA) method of improvement. Background technique [0002] Computer-aided engineering (CAE) is now being used to assist engineers in analysis, simulation, design, manufacturing, and more. In the traditional engineering design process, CAE analysis (such as finite element analysis (FEA), finite difference analysis, meshless analysis, computational fluid dynamics (CFD) analysis, used to reduce noise-vibration-harshness (NVH ) Modal analysis, etc.) have been used to evaluate the response (eg pressure, displacement, etc.). Taking automotive design as an example, FEA is used to analyze a specific version or design of a car for its response under specific load conditio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F17/50G06F17/5009G06F2217/08G06F2111/06G06F30/00G06F30/20
Inventor 图沙尔·戈尔
Owner LIVERMORE SOFTWARE TECH
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