2012Shipbuilding of ChinaRequires access

Multidisciplinary and Multi-Objective Design Optimization Based on Adaptive Weighted-Sum Method

LI Dong-qin

Open publisher page 2 citations

Abstract

To resolve the problems involving high coupling,low computation efficiency and difficulties in obtaining optimal solution with designers’ preferences,a new algorithm for collaborative optimization(CO) and multi-objective design optimization(MODO) based on adaptive weighted-sum method was proposed.The basic ideas and optimization process were presented.Closely combined with the practical engineering experiences,the MODO problems were transformed to comprehensive objective design optimization problem,featuring with designers’ preferences.Then,collaborative optimization model was constructed by adopting adaptive weighted-sum method as optimization strategy so that the optimal solution featuring with designers’ preferences was obtained.Finally,taking the Tanaka problem and multidisciplinary and multi-objective design optimization for offshore supply vessel as example,the proposed method was proved to resolve the non-inferior solution of MODO effectively.

About this research paper

What this paper is about

To resolve the problems involving high coupling,low computation efficiency and difficulties in obtaining optimal solution with designers’ preferences,a new algorithm for collaborative optimization(CO) and multi-objective design optimization(MODO) based on adaptive weighted-sum method was proposed.The basic ideas and optimization process were presented.Closely combined with the practical engineering experiences,the MODO problems were transformed to comprehensive objective design optimization problem,featuring with designers’ preferences.Then,collaborative optimization model was constructed by adopting adaptive weighted-sum method as optimization strategy so that the optimal solution featuring with designers’ preferences was obtained.Finally,taking the Tanaka problem and multidisciplinary and multi-objective design optimization for offshore supply vessel as example,the proposed method was proved to resolve the non-inferior solution of MODO effectively.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

To resolve the problems involving high coupling,low computation efficiency and difficulties in obtaining optimal solution with designers’ preferences,a new algorithm for collaborative optimization(CO) and multi-objective design optimization(MODO) based on adaptive weighted-sum method was proposed.The basic ideas and optimization process were presented.Closely combined with the practical engineering experiences,the MODO problems were transformed to comprehensive objective design optimization problem,featuring with designers’ preferences.Then,collaborative optimization model was constructed by adopting adaptive weighted-sum method as optimization strategy so that the optimal solution featuring with designers’ preferences was obtained.Finally,taking the Tanaka problem and multidisciplinary and multi-objective design optimization for offshore supply vessel as example,the proposed method was proved to resolve the non-inferior solution of MODO effectively.

Key concepts: Multidisciplinary design optimization, Mathematical optimization, Engineering optimization, Optimization problem, Computation, Computer science, Continuous optimization, Adaptive optimization

Related papers

Back to paper searchBrowse research topicsOriginal source
Multidisciplinary and Multi-Objective Design Optimization Based on Adaptive Weighted-Sum Method — Research Paper | ScholarLens