An enhanced genetic algorithm–based multi-objective design optimization strategy-Reference-Cited by-同舟云学术

An enhanced genetic algorithm–based multi-objective design optimization strategy

Published:2018-07 Issue:7 Volume:10 Page:168781401878483
ISSN:1687-8140
Container-title:Advances in Mechanical Engineering
language:en
Short-container-title:Advances in Mechanical Engineering

Author:

Yuan Rong¹²,Li Haiqing³^ORCID,Wang Qingyuan¹²

Affiliation:

1. School of Mechanical Engineering, Chengdu University, Chengdu, China

2. College of Architecture and Environment, Sichuan University, Chengdu, China

3. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China

Abstract

In this study, an enhanced genetic algorithm is proposed to solve multi-objective design and optimization problems in practical engineering. In the given approach, designers choose available design results from the given samples first. These samples are re-ordered according to their mutual relationships. After that, designers choose an exact ratio of conformity as available field. Furthermore, more weight information can be obtained through finding the minimum value of the norm of unconformity and satisfactory samples. These samples can be used to reflect the preference chosen for Pareto design solutions. A structure design problem of speed increaser used in wind turbine generator systems is solved to show the application of the given design strategy.

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Publisher

SAGE Publications

Subject

Mechanical Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1177/1687814018784836

Reference34 articles.

1. A multi-objective optimization problem for multi-state series-parallel systems: A two-stage flow-shop manufacturing system