Modified Particle Swarm Optimization Algorithm for Multi-Objective Optimization Design of Hybrid Journal Bearings

被引：17
作者：
Chan, Chia-Wen [1 ]
机构：
[1] Natl Chung Cheng Univ, Dept Mech Engn, Adv Inst Mfg High Tech Innovat, Ming Hsiung 621, Chia Yi, Taiwan
来源：
JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME | 2015年 / 137卷 / 02期
关键词：
GENETIC ALGORITHM;
D O I：
10.1115/1.4028606
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
The objective of design optimization is to determine the design that minimizes the objective function by changing design variables and satisfying design constraints. During multi-objective optimization, which has been widely applied to improve bearing designs, designers must consider several design criteria or objective functions simultaneously. The particle swarm optimization (PSO) method is known for its simple implementation and high efficiency in solving multifactor but single-objective optimization problems. This paper introduces a new multi-objective algorithm (MOA) based on the PSO and Pareto methods that can greatly reduce the number of objective function calls when a suitable swarm size is set.
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页数：7
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