Robust Optimization of Adjustable Control Factors Using Particle Swarm Optimization

2013 ◽  
Author(s):  
Takeo Kato ◽  
◽  
Koichiro Sato ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Control Factors

scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Significant Factors Identification for Particle Swarm Optimization Algorithm to Solving the Design Optimization Problem of a Four-Bar Linkage for Path Generation

2012 ◽  
Vol 249-250 ◽  
pp. 1180-1187 ◽  
Author(s):  
Cheng Kang Lee ◽  
Yung Chang Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Path Generation ◽  
Swarm Optimization ◽  
Control Factors ◽  
Four Bar Linkage

Particle swarm optimization (PSO) is a well-known population-based searching algorithm to solving optimization problems. This paper aims at identifying significant control factors for PSO to solving the design optimization problem of a four-bar linkage for path generation. Control factors considered herein are inertial weight, acceleration coefficients, breeding operation, and the number of population. A full factorial design of experiments is used to construct a set of experiments. Experimental results are analyzed with the analysis of variance method. According to the results obtained in this paper, breeding operation and the interaction between breeding operation and acceleration coefficients are significant. Inertial weight, acceleration coefficients, the number of population, and the other interactions are not significant. For the design optimization problem discussed herein, it is suggested to adopt breeding operation strategy and apply constant acceleration coefficients to increase significantly PSO’s performance and robustness. Type of inertial weight and the number of population do not affect PSO’s performance and robustness significantly.

Identifying Significant Control Factors of Particle Swarm Optimization Algorithms in Solving Permutation Flowshop Scheduling Problems

2015 ◽  
Vol 710 ◽  
pp. 61-66
Author(s):  
Cheng Kang Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Swarm Optimization ◽  
Significant Control ◽  
Inertia Weight ◽  
Control Factors ◽  
Searching Ability

This paper aims to identify significant control factors of particle swarm optimization (PSO) algorithms in solving permutation flowshop scheduling problems. Control factors of PSO algorithms considered herein include inertial weight, acceleration coefficients, breeding operation, and the amount of particles. The full factorial design method is applied to plan a set of experiments. Each experiment, denoting a specific version of PSO algorithm, is used to solve the test problems, Carlier problems. The searching ability of PSO algorithms is defined by the ratio of the number of times that the optimal makespan is searched to the total number of searching times. To identify significant factors, the analysis of variance (ANOVA) method is used to analyze the results of experiments. According to the results of ANOVA, adopting time-varying acceleration coefficients, breeding operation, and a low amount of particles can advance significantly the searching ability of PSO algorithms. Adopting a high amount of particles can increase significantly the robustness of PSO algorithms. Any two-factor interaction is not significant. Inertia weight is not a significant factor, so any effort to modify inertia weight is unnecessary.

Robust optimization using multi-objective particle swarm optimization

2009 ◽  
Vol 14 (2) ◽  
pp. 174-177 ◽  
Author(s):  
Satoshi Ono ◽  
Yohei Yoshitake ◽  
Shigeru Nakayama
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective
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