A Multi-Objective Optimization Design of a Drum Brake Based on Differential Evolution and Particle Swarm Optimization

2016 ◽  
Author(s):  
Guest Editor Jianping Du
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Design ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Drum Brake

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.

scholarly journals Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm

2021 ◽  
Vol 11 (3) ◽  
pp. 1107
Author(s):  
Miloš Sedak ◽  
Božidar Rosić
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Planetary Gearbox ◽  
Multi Objective ◽  
Mutation Operators ◽  
Evolution Algorithm

This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions.

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.

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