Multi-objective optimization design of the heating/cooling channels of the steam-heating rapid thermal response mold using particle swarm optimization

2011 ◽  
Vol 50 (5) ◽  
pp. 790-802 ◽  
Author(s):  
Guilong Wang ◽  
Guoqun Zhao ◽  
Huiping Li ◽  
Yanjin Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Thermal Response ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Steam Heating ◽  
Multi Objective ◽  
Cooling Channels

Application of Particle Swarm Optimization in Cylinder Helical HGearH Multi-Objective Design

2011 ◽  
Vol 474-476 ◽  
pp. 2229-2233
Author(s):  
Yuan Bin Mo ◽  
Ji Zhong Liu ◽  
Bao Lei Wang ◽  
Wei Min Wan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

Cylinder helical gGear is widely used in industry. Multi-objective optimization design of the component is often met in its different application sSituation. In this paper a novel multi-objective optimization method based on Particle Swarm Optimization (PSO) algorithm is designed for applying to solve this kind of problem. A paradigm depicted in the paper shows the algorithm is practical.

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

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Chia-Wen Chan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Design ◽  
High Efficiency ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Modified Particle Swarm Optimization

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.

Sign in / Sign up

Export Citation Format

Share Document